Aalto Science Institute

AScI internships: Old summer 2020 available positions

These research projects were offered in the AScI international internship programme 2020. (This will be replaced with the 2021 positions at the end of Dec 2020).

Below are the 49 available research projects for summer 2020

(This will be updated with the 2021 positions at the end of Dec 2020).

Applications closed on 17 January 2020.

Please note that this old list of projects is purely for general information and may differ significantly from what is offered in 2021.

How to apply to the AScI internship programme
  • # = project/topic number (these are the numbers you enter in the "other info" section of the application form)
  • CHEM = School of Chemical Engineering
  • ELEC = School of Electrical Engineering
  • ENG = School of Engineering
  • SCI = School of Science

#

School

Field of Study

Info

Project Description

1

CHEM

Electrochemistry

Title: Investigation of ageing processes in Li-ion batteries

Sub-Field:

Department: Chemistry and Materials Science

Professor in Charge: Tanja Kallio ([email protected])

Academic Contact: Ekaterina Fedorovskaya ([email protected])

Description: In this project, investigation of ageing of Li-ion batteries comprising of advanced materials is carried out using an accelerated aging test and related electrochemical and structural characterization methods. The positive and negative electrode materials are extracted and investigated before and after ageing using various structural and electrochemical characterization methods such as Fourier transform infrared spectroscopy, Raman spectroscopy, X-ray photoelectron spectroscopy, Scanning electron microscopy, Transmission electron microscopy, cyclic voltammetry, rate capability measurements, electrochemical impedance spectroscopy.

Skills: Knowledge and experience on using of the above-mentioned characterization methods as well as knowledge and experience on inorganic synthesis methods are appreciated. Furthermore, experience on using a glove box and assembling of electrochemical cells, such supercapacitors and Li-ion batteries, is valued.

Education: The appropriate candidate has background in Chemistry, Material Science or related fields. Completing courses on Analytical chemistry, Inorganic Chemistry, Organic Chemistry, Electrochemistry, Physical Chemistry, Chemical Kinetics, Chemical Thermodynamics, Chemistry of Solids, Materials Science, Materials for Electrochemical Engineering is beneficial.

16

CHEM

Materials Science

Title: Stretchable Organic Devices

Sub-Field:

Department:

Professor in Charge:

Academic Contact:

Description: 

Please refer to the same project number under ELEC for details.

This is a duplicate listing for project #16. It is listed separately so that it shows up under CHEM as well as ELEC since it is a joint project between the two schools.

 

3

CHEM

Metallurgy

Title: Oxy-carbo-nitride equilibria in ferro-titanium alloy

Sub-Field:

Department: Chemical and Metallurgical Engineering

Professor in Charge: Daniel Lindberg ([email protected])

Academic Contact: Min Paek ([email protected])

Description: This project aimed at developing the thermodynamic database of Fe-Ti-O-N-C system for the optimization of ferro-titanium alloy production. The detrimental Ti inclusions cannot be completely avoided due to the high affinity of Ti with the gaseous impurities such as O, N and C. Since the solubility limit of such impurities has great influence on the quality of the ferro-titanium alloy, the equilibrium reaction between the non-metallic inclusion and the liquid Fe-Ti alloy has to understood. However, the studies on the complex structure of Ti oxy-carbo-nitride formation in liquid Fe-Ti alloy are very scarce.

In order to understand the various phenomena during the Fe-Ti alloy production, accurate thermodynamic information of O, N and C in Fe-Ti melt will be measured over the entire Ti concentration range. The experimental results can be used to analyze the thermodynamic interactions among the alloying elements and the impurities.

Preferred qualifications:
1. Strong background in thermodynamics – solution chemistry
2. Experience of equilibrium experiment using high-temperature registance and induction furnaces
3. Experience of analysis – EPMA, DSC, ICP
4. Skills in computational thermodynamics – CALPHAD method, FactSage software

4

CHEM

Metallurgy

Title: Thermodynamic Modeling of the Fe-V-N-C system

Sub-Field:

Department: Chemical and Metallurgical Engineering

Professor in Charge: Daniel Lindberg ([email protected])

Academic Contact: Min Paek ([email protected])

Description: This topic focuses on developing the predictive tools of vanadium extraction from industrial wastes by the smelting process. The secondary resources containing vanadium is smelted directly in the electric arc furnace. During the process, the alloy can be contaminated with carbon from the electrolyte and nitrogen from air, respectively. Even though vanadium is well known to form carbide and nitride as well as complex carbo-nitride solid solution, its formation and phase equilibria in the Fe-V-N-C system have not been clearly described. Thermodynamic modeling of the sub-ternary systems will be conducted during summer.
An applicant should be familiar with the FactSage thermochemical software. Understanding of the fundamental and solution thermodynamics are also strongly required.

5

ELEC

Computer Science

Title: Addressing uncertainty in 2D gesture input

Sub-Field: Human–Computer Interaction

Department: Communications and Networking

Institute: Finnish Center for AI (fcai.fi)

Professor in Charge: Antti Oulasvirta ([email protected])

Academic Contact: Luis A. Leiva ([email protected])

Description: Stroke gestures represent 2D symbols and shapes that are mapped to user interface commands, typically those commands that are very repetitive (e.g. dialing up a family member) or tedious to access (e.g. selecting an option from a multi-level menu). Stroke gesture input relies on gesture recognizers to decide which command should be triggered. Because gesture recognizers are not error-free, unintended or potentially harmful commands could be triggered involuntarily. In such cases, it would be useful to ask the user to disambiguate among perceptually similar gestures. But it is important not to overwhelm the user and only ask as few times and as little information as possible.

The intern will work on the study and implementation of a prototype that aims to answer the following research questions: When should the user be asked for disambiguation? How much information should be presented to the user to disambiguate? The intern is expected to be knowledgeable in statistics and machine learning, and have solid programming skills in JavaScript and/or Python.

6

ELEC

Computer Science

Title: Automated UI Layout system

Sub-Area: Human–Computer Interaction

Department: Communications and Networking

Institute: Finnish Center for AI (fcai.fi)

Professor in Charge: Antti Oulasvirta ([email protected])

Academic Contact: Niraj Ramesh Dayama ([email protected])

Description: Researchers at the User Interfaces group in Aalto University have developed efficient mathematical models to assist in the automated design of graphical user interfaces (GUIs). The resulting GUIs are assured to satisfy several important criterion. These mathematical models now require to be translated into a flexible interface for use of the wider academic/research community.

This translation of models into a usable interface will require understanding of Python, Java and web technologies. The students will also gain insights in combinatorial optimization techniques including mixed integer linear programming and genetic algorithms.

The intern will work on the study and implementation of optimization techniques for an industry sponsored project related to Graphical User Interface design. Specifically, integer programming and heuristic algorithms will be extensively required. The intern will need to have a strong previous background in coding, especially in Python and Java. Knowledge of web technology, Sketch application and any UI design will be an added advantage.

7

ELEC

Computer Science

Title: Data visualization technique for neighbourhood clustering

Sub-Field: Mathematics, Geometric Algorithms, Optimization Techniques

Department: Communications and Networking

Institute: Finnish Center for AI (fcai.fi)

Professor in Charge: Antti Oulasvirta ([email protected])

Academic Contact: Niraj Ramesh Dayama ([email protected])

Description: This project intends to develop a deterministic and robust technique to visualize multi-dimensional data in form of nodes separated by specified distances. This will be a rigorous formalization of force-directed graphs, but we are exploring alternatives to spring energy minimization type of approaches. We intend to develop and implement algorithms for finding a unique data representation and then to prove that said representation is indeed non-dominated.

The intern will work on the study and implementation of optimization techniques, mathematical programming and possibly geometric algorithms. Specifically, integer programming and heuristic algorithms will be extensively required. The intern will need to have a strong previous background in coding, especially in Python or Java.

8

ELEC

Computer Science

Title: Deep generative handwriting models

Sub-Field: Human–Computer Interaction

Department: Communications and Networking

Institute: Finnish Center for AI (fcai.fi)

Professor in Charge: Antti Oulasvirta ([email protected])

Academic Contact: Luis A. Leiva ([email protected])

Description: The articulation and production of human movements is a complex problem grounded on motor control principles. Modern deep generative models can produce realistic human-like movements, exploiting non-linear and long-term dependencies within the observed human data. However, these models can make limited inferences about the distribution of the observed data. This topic will focus on modeling human handwriting data, namely 2D trajectories with associated timestamps.

The intern will contribute techniques to sample realistic handwriting data from deep generative models. The intern is expected to be knowledgeable in Deep Learning (DL) methods for generative modeling and be familiarized with some popular DL library or framework, such as Tensorflow, Keras, Pytorch, Lightning, etc.

9

ELEC

Computer Science

Title: Helping People to Learn with Artificial Teachers that Adapt to User’s Cognitive Characteristics

Sub-Field: Human-Computer Interaction, Machine Learning, and Cognitive Science

Department: Communications and Networking

Institute: Finnish Center for AI (fcai.fi)

Professor in Charge: Antti Oulasvirta ([email protected])

Academic Contact: Aurélien Nioche ([email protected])
 

Description: Leitner-based algorithms, or so-called spaced repetition algorithms, have been used for decades in flash-card applications, by using a simple rule to schedule the reviews: in case of success, review frequency is lowered while in case of failure, it is increased. Although robust and computationally not expensive, this algorithm is utterly blind to the individual cognitive features of the user. We propose an alternative algorithm that aims to adapt individually to each user. The algorithm that we suggest split the problem into two parts: (i) inference of the cognitive features, (ii) Sequence planning. Hence, on the one hand, we infer the cognitive traits of the user, by fitting the user data online (i.e., while the user is using the application) with a series of plausible psychological models, to determine the adequate model and parametrization to describe the user. On the other hand, the schedule of review is optimized based on individualized predictions of performance. The main challenges are the fidelity of the fit given the data scarcity for the first part, and the combinatorial explosion for the second part.
 
This project includes in silico experiments (with artificial agents only) and lab experiments (with human embodied users). The intern will aim to improve the user’s cognitive models and/or improving the algorithm that handles the sequence planning. The intern will be invited to also test its own hypothesis. The intern is expected to know Python and to be proficient in data analysis. Having some knowledge about cognitive modeling is not mandatory but will be appreciated.

10

ELEC

Computer Science

Title: Intelligent Tools for User Interface Design and Exploration

Sub-Field: Human–Computer Interaction

Department: Communications and Networking

Professor in Charge: Antti Oulasvirta ([email protected])

Academic Contact: Kashyap Todi ([email protected])

Institute: Finnish Center for AI (fcai.fi)

Description: The design of user interfaces (UIs) is a complex and challenging task, requiring professional expertise and design knowledge. As there can be several possible good solutions for any design task, it is challenging to manually explore all such alternatives. Additionally, the usability of a design is highly dependent on the familiarity of users with similar designs – if a user has used similar UIs before, it is likely that the new design will be easier to navigate and use.

Computational design using techniques such as optimisation have previously supported designers in exploring several design solutions within a short amount of time. One such example is a design tool that we previously developed: www.kashyaptodi.com/sketchplore. The tool automatically suggests new UI designs that improve the usability of existing designs, and also recommends diverse alternative designs.

Building upon our prior work, you will get the opportunity to design and implement an exciting new intelligent tool for UI design and exploration. During the internship, you will combine our existing framework for UI layout optimisation with machine learning techniques that can improve solutions by using knowledge from existing UI designs. This could be using freely available datasets such as Rico (interactionmining.org/rico), or by mining designs from the web. The aim of the intelligent tool will be to suggest design solutions that are both (1) optimised for usability and aesthetics, and (2) familiar to users.

References:
Kashyap Todi, Daryl Weir, and Antti Oulasvirta. Sketchplore: Sketch and Explore with a Layout Optimiser. In Proc. of DIS ’16. DOI: https://doi.org/10.1145/2901790.2901817
Kashyap Todi, Jussi Jokinen, Kris Luyten, and Antti Oulasvirta. Familiarisation: Restructuring Layouts with Visual Learning Models. In Proc. of IUI ‘18. DOI: https://doi.org/10.1145/3172944.3172949

Prerequisites:
GUI programming skills is required (e.g. Objective-C, Swift, Qt, JavaScript); some experience with machine learning methods is beneficial (optional).

11

ELEC

Computer Science

Title: Interactive multi-objective optimization

Sub-Field: Mathematics, Machine learning, Optimization Techniques

Department: Communications and Networking

Institute: Finnish Center for AI (fcai.fi)

Professor in Charge: Antti Oulasvirta ([email protected])

Academic Contact: Morteza Shiripour ([email protected])

Description: This project aims to develop an interactive multi-objective to graphical user interfaces.  Graphical user interface is a visual way for the user to interact with a computer via several graphical elements (e.g. icons, menus, windows and buttons) and its layout problem aims to find the best way to organize these graphical elements on a fixed canvas. Organizing these layouts is challenging because there are a huge number of different layout designs and the designers should also consider various limitations and criteria in their layout design process. Hence, we intend to present a model and algorithm in order to automatically generate layouts addressing different multiple tasks such as usability and aesthetic qualities.  Moreover, we aim to guide the algorithm to find solutions according to a decision maker preferences.

Prerequisites:
The successful intern should have a background in mathematical programming and evolutionary multi-objective optimization, a strong previous background in coding in Python, machine learning methods is beneficial (optional).

12

ELEC

Computer Science

Title: Robotic Simulation of Human Active Touch

Sub-Field: Machine Learning, Computer Vision, and Robotics

Department: Communications and Networking

Institute: Finnish Center for AI (fcai.fi)

Professor in Charge: Antti Oulasvirta ([email protected])

Academic Contact: Yi-Chi Liao ([email protected])

Description: Active touch sensing, the integration of movement and haptic sense, is a critical information-seeking behavior for humans to interact with the world (see ref: https://bit.ly/2rKSq3H). In comparison to passive touch (tactile sensing with no relative movements between humans and the actuators), active sensing is a more common and effective channel for recognizing and manipulating objects. Thanks to advancements in machine learning, new frameworks have been proposed for transferring complex human active-touch behaviors into robotic applications (see ref: https://bit.ly/2rElR7I).

The aim of this project is to create models that allow a robot to explore environments and learn how to use interfaces (e.g., mobile device, keyboard, mouse) with haptic information. As an intern, you will get the opportunity to work on a cutting-edge research topic and implement some parts of the model that will be applied to both virtual environment and physical robotic simulation. The intern is expected to have basic knowledge in at least one of the following domains: (1) Reinforcement Learning, e.g., DQN, DDPG, etc, (2) Computer Vision for real-time detection, e.g., YOLO, RCNN, etc, and/or (3) Robotic control and simulation, e.g., Mujoco. Prior experience in programming is also required.

13

ELEC

Computer Science

Title: Self-Adapting User Interfaces

Sub-Field: Human–Computer Interaction

Department: Communications and Networking

Institute: Finnish Center for AI (fcai.fi)

Professor in Charge: Antti Oulasvirta ([email protected])

Academic Contact: Kashyap Todi ([email protected])

Description: Adaptive interfaces change an application’s visual layout and behaviour depending on the user’s context, skills, or abilities. By continually capturing some information from the user, and modelling them, they can modify their appearance or some characteristics over time, thus improving usability. Some examples of such interfaces that we have previously developed are (1) a web browser that adapts website layouts towards the user’s familiarity (www.kashyaptodi.com/familiarisation) , and (2)  a web framework  that records user’s clicks and actions, and uses this to adapt menus by reordering items, or highlighting certain items (www.kashyaptodi.com/sam).

During this internship, you will further explore the area of self-adapting user interfaces. You will be given the opportunity to develop a self-adapting interface or tool of your own, which improves some aspect of usability. There are several possibilities for the exact goal of the designed adaptive tool, and this can be discussed at the beginning of the internship.

References:
Kashyap Todi, Jussi Jokinen, Kris Luyten, and Antti Oulasvirta. Familiarisation: Restructuring Layouts with Visual Learning Models. In Proc. of IUI ‘18. DOI: https://doi.org/10.1145/3172944.3172949
Camille Gobert, Kashyap Todi, Gilles Bailly, and Antti Oulasvirta. SAM: a modular framework for self-adapting web menus. In Proc. of IUI ‘19. DOI: https://doi.org/10.1145/3301275.3302314

Prerequisites:
GUI programming skills is required (e.g. Objective-C, Swift, Qt, JavaScript); some experience with machine learning methods is beneficial (optional).

14

ELEC

Computer Science

Title:Smart branching using machine learning

Sub-Field: Mathematics, Machine learning, Optimization Techniques

Department: Communications and Networking

Institute: Finnish Center for AI (fcai.fi)

Professor in Charge: Antti Oulasvirta ([email protected])

Academic Contact: Niraj Ramesh Dayama ([email protected])

Description: This project is an investigation of whether mixed integer linear programming techniques can be used in conjunction of machine learning algorithms to solve an emerging class of research problems. While integer programming is a well proven technique for finding optimal solutions in a wide combinatorial scenario, machine learning can assist in classifying candidate solutions based on several criteria that are not amenable to integer programming. The current project intends to utilize machine learning as an intermediate evaluator for an integer programming framework. If this framework can be proven for a classical test scenario, it can open up a new technique for a wide variety of problems in future.

The intern will work on the study and implementation of optimization techniques, mathematical programming and machine learning. Specifically, branch and bound algorithms and heuristic algorithms will be extensively required. The intern will need to have a strong previous background in coding, especially in Python or Java.

15

ELEC

Information technology

Title: IoT, Blockchains and Smart Contracts

Sub-Field:

Department: Communications and Networking

Professor in Charge: Raimo Kantola ([email protected])

Academic Contact: Dmitrij Lagutin ([email protected])

Description: Are you developer interested in blockchains, Internet-of-Things (IoT) and their real-world applications?
We are looking research assistants to participate in development work at Aalto University’s SOFIE project. In this position you will have a chance to make an impact by developing real-world IoT solutions utilizing blockchains.

SOFIE (https://www.sofie-iot.eu) is an Aalto-led European Union H2020 project researching the use of blockchains for IoT device access federation across multiple organizations, multiple blockchains and multiple IoT platforms. It enables cross-organization IoT device operation and through the use of smart contracts, enables secure, privacy-preserving, and automated access control to shared and private resources. The SOFIE solution will be piloted in four real-world pilots.

In this position you will work on interesting and challenging real-world problems using cutting-edge technologies, together with experts from all around the Europe.

The work focuses on the development of SOFIE Framework components, which will interact with blockchains and/or IoT platforms, and/or the development of smart contracts using Ethereum, Hyperledger Fabric, and/or other suitable distributed ledgers for the purpose of creating proof-of-concept contracts for SOFIE pilot projects.

We except applicants to have:
• Be at least a bachelor-level student in computer science or related field
• Good programming skills
• Solid experience with Python and Web service backend development
• Experience with Solidity and smart contracts
• Experience with distributed service design and API specification
• Good knowledge of written and spoken English
• Ability to work in an international environment
This position enables work on novel technologies including blockchains in international, real-life environment.

16

ELEC

Materials Science

Title: Stretchable Organic Devices

Sub-Field: Chemistry, physics

Department:  Electronics and Nanoengineering
&
Chemistry and Materials Science

Professor in Charge: Caterina Soldano ([email protected])
&
Jaana Vapaavuori ([email protected])

Academic Contact: Caterina Soldano ([email protected])
&
Jaana Vapaavuori ([email protected])

Description: Research groups on Organic Electronics (Prof. C. Soldano, ELEC) and Multifunctional Materials Design (Prof. J. Vapaavuori, CHEM) at Aalto University are looking for a curious and talented student, either at BSc or MSc level, for the Summer 2020. Your work will be in the framework of a joint inter-school project funded by Aalto University to create a stretchable organic devices platform. You will work in a cross-disciplinary environment across Chemistry and Physics, and your role will be to develop and characterize materials to be applied in our device platform. Experimental work will include preparation of polymer substrates, surface characterization and device fabrication.

Our group is international, communication and interaction is in English, thus a good oral and written command is required. Furthermore, previous work experience in polymer science, materials engineering or organic electronics is considered an advantage.

For further information, please contact Prof. Caterina Soldano and Prof. Jaana Vapaavuori.

50 ENG Civil Engineering Title: Scan-vs-BIM for Automated Construction Progress Monitoring

Sub-Field: Construction Automation

Department: Civil Engineering

Professor in Charge: Olli Seppänen ([email protected])

Academic Contact: Mustafa Khalid ([email protected])

Description: Automated construction progress monitoring using 3D point cloud data of construction sites has shown promise to save time, cost and labor in construction projects. Scan-vs-BIM is a progress monitoring approach in which as-built models are aligned and compared with as-designed BIM models. In this project, the intern will be expected to test state-of-the-art Scan-vs-BIM approaches on 3D point cloud data obtained from construction sites.

Two skills are required:
-Good programming skills in Python
-Some experience working with 3D point cloud data

17

SCI

Biomedical Engineering

Title: Advanced techniques in nonlinear ultrasonics for cancer management

Sub-Field:

Department: Neuroscience and Biomedical Engineering

Professor in Charge: Heikki J. Nieminen ([email protected])

Academic Contact: Balasubramanian Nallannan ([email protected])

Description: High-intensity ultrasonics is an emerging field that allows one to e.g. heat tissue locally or deliver drugs/genes non-invasively and locally through skin for therapeutic purposes. The internship work is aimed at advancing a new non-linear ultrasonic method that can be applied in management of cancer.

Skills / prerequisites :
Candidates with Acoustics Engineering, Electrical Engineering, Mechanical Engineering or Science background are preferred, with one or more of the following skills / prerequisites.
• Prior exposure in handling ultrasonic actuators
• Basic knowledge in fluid mechanics
• Basic knowledge in acoustics
• Basic knowledge in electronic circuits
• Simulation exposure in using Comsol software could be an added advantage

18

SCI

Computer Science

Title: Active learning for interactive AI

Sub-Field: Machine Learning

Department: Computer Science

Professor in Charge: Samuel Kaski ([email protected])

Academic Contact: Samuel Kaski ([email protected])

Description:
Most machine learning systems operate with us humans, to augment our skills and assist us in our tasks. In environments containing human users, or, more generally, intelligent agents with specific goals and plans, the system can only help them reach those goals if it understands them. Since the goals can be tacit and changing, they need to be inferred from observations and interaction. We develop the probabilistic interactive user models and inference techniques needed to understand other agents and how to assist them more efficiently. We are looking for a student to join us in developing these techniques.

Additional keywords: active learning, experimental design, knowledge elicitation, multi-agent learning, machine teaching.

Link: https://aaltopml.github.io/machine-teaching-of-active-sequential-learners/

46 SCI Computer Science Title: Simulator-based inference

Sub-Field: Machine Learning

Department: Computer Science

Professor in Charge: Samuel Kaski ([email protected])

Academic Contact: Samuel Kaski ([email protected])

­Description: For this project you will join the Aalto Probabilistic Machine Learning Group (http://research.cs.aalto.fi/pml/) to develop probabilistic models and inference techniques. We are developing likelihood-free techniques for inference in simulator-based models. The work is basic research in machine learning and inference, with applications chosen to match your interests. We have particularly exciting work on-going in modeling of human behavior and precision medicine. For more information see our ABC software ELFI http://elfi.ai and for instance http://www.jmlr.org/papers/v19/17-374.html , https://academic.oup.com/sysbio/article/66/1/e66/2420817.

Students with strong background in mathematics and interest in model development are especially encouraged to apply. Skills and interest in programming are a big plus.

Link: https://research.cs.aalto.fi/pml/

19

SCI

Computer Science

Title: Camera-display communication with smartphones

Sub-Field:

Department: Computer Science

Professor in Charge: Mario Di Francesco ([email protected])

Academic Contact: Maria Lorena Montoya Freire ([email protected])

Description: Camera-display communication is a very interesting paradigm for exchanging data between devices (particularly, smartphones) by using their screen (as a transmitter) and their camera (as a receiver). This form of communication has a potential for several applications, for instance, those leveraging bi-directional data exchange. However, significant challenges related to both dependability and ease of use need to be overcome for practical use. The goal of this project is to extend an existing prototype for camera-display communication with focus on its performance (namely, throughput and reliability) as well as usability in real settings.

Required skills: experience with Android application development; some background on networking protocols and human-computer interactions.

Desired skills: familiarity with qualitative research methods and experience in conducting user studies; as an alternative, proficiency in C/C++ development and Android NDK.

20

SCI

Computer Science

Title: Computer Assisted Bayesian Modeling Workflow

Sub-Field: Probabilistic modelling

Department: Computer Science

Professor in Charge: Aki Vehtari ([email protected])

Academic Contact: Aki Vehtari ([email protected])

Description: You will participate in a research project in which we will develop theory and methods for a principled and robust computer assisted Bayesian modeling workflow. To guarantee wide applicability of the project results in data science industry and academic research, the novel methods will be evaluated on a range of practical machine learning models and implemented as part of the leading open-source probabilistic programming systems. Prerequisite is knowledge Bayesian methods and probabilistic programming. Experience with Stan is preferred but not mandatory.

21

SCI

Computer Science

Title: Content optimization of tiled pervasive displays

Sub-Field:

Department: Computer Science

Professor in Charge: Mario Di Francesco ([email protected])

Academic Contact: Maria Lorena Montoya Freire ([email protected])

Description: Pervasive displays are widely employed in public spaces to convey information to users. For instance, they are deployed in airports to provide information about flights status and in malls to show offers available at the stores therein. However, displays tend to be ignored by users if they are found to be neither informative nor interesting. Thus, a key challenge is the design of solutions that provide interesting content to users with limited time and attention. In particular, content selection is particularly challenging for tiled pervasive displays that show multiple content items at the same time. The goal of this project is to extend an existing approach developed in our group which relies on the information foraging theory for adaptive selection of display content based on audience data. The project involves analysis of data collected from a depth camera as well as the evaluation of the improved solution under real settings.

Required skills: proficiency in Python and Javascript; some experience with data analysis.

Desired skills: some knowledge on human computer interaction.

22

SCI

Computer Science

Title: Design A Visualisation Tool for DNA Strand Displacement
Systems

Sub-Field:

Department:  Computer Science

Professor in Charge: Pekka Orponen ([email protected])

Academic Contact: Vinay Gautam ([email protected])

Description: DNA strand displacement (DSD) systems have emerged as a successful methodology for the design of DNA-based molecular systems with programmable dynamic behaviours. DSD systems consist of a set of rationally-designed short DNA strands that interact through a common but programmable underlying mechanism, termed toehold-mediated DNA strand displacement. A design and simulation tool for DSD systems, provided it can automate the complex and tedious design process of DNA strands and helps in analysing behaviours of DSD systems using simulation, has the potential to offer significant insight into these systems.

We have developed a modelling and simulation tool for DSD systems, referred to as RuleDSD (https://github.com/ashleylst/DSDPy). We need your help to develop a graphical user interface for rendering of DNA strands, complexes and their networks generated from the RuleDSD. In this summer internship project, intern will develop a graphical user interface that is to be integrated with the existing RuleDSD engine.

We are looking for second or third year student with good programming skills and an understanding of algorithms. Previous knowledge of biomolecules is not necessary, although it is an asset. The work is performed in the context of research project “Algorithmic designs for biomolecular nanostructures (ALBION)”, funded by the Academy of Finland. For further information, please see the research group webpage at http://research.ics.aalto.fi/nc/ .

23

SCI

Computer Science

Title: Distributed deep learning inference in fog networks

Sub-Field:

Department: Computer Science

Professor in Charge: Mario Di Francesco ([email protected])

Academic Contact: Thaha Mohammed ([email protected])

Description: The proliferation of resource-constrained mobile devices and smart objects in the Internet of Things has led to the generation of a large amount of data. Due to the recent advancements in Deep Learning (DL), services and applications based on Artificial Intelligence (AI) have become an enabler of smart cities, factories, intelligent transport systems and much more. DL models are often built from collected data (training), to enable the detection, classification, and prediction of future events (inference). Due to the limited computing resources at end devices, these models are often offloaded to powerful computing nodes such as cloud servers. However, it is difficult to satisfy latency, reliability, and bandwidth constraints while offloading data to cloud servers for training and inference of AI models. Thus, in recent years, AI services and tasks have been pushed closer to the end users – to the fog – to meet these requirements. The main objective of this project is to implement a DNN inference offloading framework over fog networks developed in our research group and to evaluate its performance.

Required skills: background in deep learning and distributed computing; proficiency in Python.

Desired skills: proficiency in any deep learning framework and Docker containers.

24

SCI

Computer Science

Title: Machine learning with probabilistic principles

Sub-Field: Machine Learning

Department: Computer Science

Professor in Charge: Arno Solin ([email protected])

Academic Contact:William Wilkinson ([email protected])

Description: My research group is looking for motivated, skilled, and open-minded summer students with an interest in real-time inference and application of probabilistic machine learning methods to practical applications. Depending on the background and interests of the student, this project can be either more applied or leaning more towards theory and methods development. This project builds on recent progress in Bayesian methods in deep learning, real-time inference and sensor fusion (see recent publications at http://arno.solin.fi). The student would get hands-on experience in Bayesian methods, Gaussian processes, stochastic differential equations, deep learning, and coding (primarily Python). Successful applicants are expected to have an outstanding record in computer science, mathematics, statistics, or a related field, and familiarity with some of the topics mentioned above.

25

SCI

Computer Science

Title: Massively Parallel Algorithms for Graph Problems

Sub-Field:

Department: Computer Science

Professor in Charge: Jara Uitto ([email protected])

Academic Contact: Jara Uitto ([email protected])

Description: Parallel processing of data and distributed computing are gaining attention and becoming more and more vital as the data sets and networks we want to process are overgrowing the capacity of single processors. To understand the potential of modern parallel computing platforms, many mathematical models have emerged to study the theoretical foundations of parallel and distributed computing. In this project, we study algorithm design in these models with a particular focus on the Massively Parallel Computing (MPC) and Local Computation Algorithms (LCA) models.

The problems we study are often in (but not limited to) the domain of graphs, that serve as a very flexible representation of data. We are interested in, for example, the computational complexities of classic problems such as finding large independent sets, matchings, flows, clustering problems, etc.

The applicant is assumed to have a solid knowledge of mathematics, knowledge on the basics of graph theory, and a good command of English. No prior knowledge in distributed computing is required, although it might be helpful.

26

SCI

Computer Science

Title: Predicting structured output with deep kernel regression models

Sub-Field: Machine learning

Department: Computer Science

Professor in Charge: Juho Rousu ([email protected])

Academic Contact: Juho Rousu ([email protected])

Description: Structured output prediction is a supervised machine learning problem where both input and output variables are complex structured objects such as sequences, trees or graphs. One approach to structured output prediction consist in two steps: first solve a regression problem in a well-chosen output feature space and then solve a pre-image problem to get a solution in the original output space. Recently, this kind of approach has been extended to deep learning, allowing the methods to model complex non-linear dependencies and potentially higher predictive accuracy.

The task of the intern is to study the performance of novel deep kernel regression models (Laforgue et al. 2019) on datasets arising from biomedicine, in particular small molecule identification, where regression based structured output methods currently are state-of-the-art (Brouard, et al. 2016). The project is a part of an on-going research collaboration between KEPACO group at Aalto (Prof. Juho Rousu) and the Chair Data Science & Artificial Intelligence in Telecom Paris (Prof. Florence d’Alché-Buc). The internship gives good possibilities for continuing as a thesis project and Ph studies

Prerequisite skills: calculus, linear algebra and basic probability theory, algorithms, programming skills, basics in machine learning and/or data science.

References:
Brouard, C., Shen, H., Dührkop, K., d’Alché-Buc, F., Böcker, S. and Rousu, J., 2016. Fast metabolite identification with input output kernel regression. Bioinformatics, 32(12), pp.i28-i36.
Pierre Laforgue, Stephan Cléençon, Florence d’Alché-Buc, Autoencoding any Data through Kernel Autoencoders. AISTATS 2019: 1061-1069.

27

SCI

Computer Science

Title: Signal classification for cross-technology interference detection

Sub-Field:

Department: Computer Science

Professor in Charge: Mario Di Francesco ([email protected])

Academic Contact: Verónica  Toro Betancur ([email protected])

Description: The Internet of Things (IoT) has motivated employing different technologies to satisfy diverse application-specific requirements. As a result, heterogeneous networks have to coexist in the same physical space and share the same frequency band, resulting in throughput degradation. Several works in the literature have addressed the problem of detecting interfering technologies; however, they focus on one or a few technologies. Moreover, the use of Machine Learning (ML) techniques as a tool to classify the signals and detect their native technology has not been carefully studied, even though it is recognized as a promising approach. The goal of this project is to implement a platform-independent ML algorithm to classify foreign interfering technologies, such as WiFi, ZigBee, Bluetooth, and so on. The project involves carrying out both simulations and experiments with software-defined radios to show the effectiveness of the algorithm.

Required skills: background in wireless communications and signal processing; proficiency in Python or C++.

Desired skills: experience with software-defined radios

28

SCI

Computer Science

Title: Software for the design of DNA nanostructures

Sub-Field: Software Engineering

Department: Computer Science

Professor in Charge: Pekka Orponen ([email protected])

Academic Contact: Pekka Orponen ([email protected])

Description: The area of DNA nanotechnology [1] employs DNA as generic building material for assembling nanoscale objects with dimensions in the order of 10-100 nanometres. In a significant contribution to this area, our group presented in 2015, together with a biochemistry team from Karolinska Institutet in Stockholm, a general technique for rendering almost arbitrary 3D wireframe designs into biomolecules folded from a single long DNA strand [2]. Our method, which has since then been widely applied and cited in international publications almost 300 times, is presently available as part of the DNA nanostructure design suite vHelix, hosted at the Karolinska Institutet [3].

The task in the present project is to improve the versatility and usability of the vHelix suite, by (i) separating the graphical and DNA design parts of the software into different modules, (ii) developing simple input and output interfaces to the DNA design module, so that it can be conveniently integrated to different graphical design tools, and (iii) developing a web interface for submitting graphical designs for processing on a server-based DNA design module.

The project requires good programming skills and preferably some experience in software-engineering type of work. Previous knowledge of biomolecules is not necessary, but familiarity with 3D design suites such as Maya or Blender is an asset. The work is performed in the context of research project “Algorithmic designs for biomolecular nanostructures (ALBION)”, funded by the Academy of Finland. For further information, please see the research group webpage at http://research.cs.aalto.fi/nc/ .

Online sources:
[1] https://en.wikipedia.org/wiki/DNA_nanotechnology
[2] https://www.aalto.fi/en/news/nanostructures-can-now-be-3d-printed-using-dna
[3] http://vhelix.net/

29

SCI

Computer Science

Title: Statistical or psychological theories for user security

Sub-Field:  Computer security, statistics, psychological science, social and behavioral sciences, cognitive science, computer engineering

Department: Computer Science

Professor in Charge: Janne Lindqvist ([email protected])

Academic Contact: Janne Lindqvist ([email protected])

Description: The Human-Computer Interaction and Security Engineering Lab http://lindqvistlab.org at the Aalto Department of Computer Science is recruiting a summer intern. We will be recruiting a student to contribute to our research on the application of statistical theory or psychological theory to the study of user security. For example, we will study the cognitive mechanisms responsible for password usability and the psychological principles driving security decisions. Our project will engage the summer intern in every step of the research process including research design, statistical analysis and provide the intern with a unique exposure to the application of psychology and statistics in human-computer interaction and usable security. Projects may also include Bayesian data analysis. Accepted student also has the opportunity to participate to activities of Helsinki-Aalto Center for Information Security (HAIC).

30

SCI

Computer Science

Title: Volumetric data streaming from smartphones

Sub-Field:

Department: Computer Science

Professor in Charge: Mario Di Francesco ([email protected])

Academic Contact: Gopika Premsankar ([email protected])

Description: Smartphones are increasingly being equipped with depth sensors, which measure depth directly and thus can be used in 3D reconstructions as well as volumetric streaming. In fact, recently, Samsung has recently released 3D scanning app that runs on their latest Samsung Galaxy Note and S10 5G smartphones. This application creates small-scale 3D models on the phone. The rise of 5G and edge computing will play a key role in enabling applications that can process large-scale depth data offloaded from smartphones. For instance, tele-presence applications can directly use depth data to reconstruct 3D models of people to enable new and immersive forms of communication. Another promising application is the real-time streaming of the 3D environment in which a user is present. The goal of the project is to evaluate how to offload depth data from smartphones and to build a simple application that relies on such data generated by smartphones. The project will investigate the wireless bandwidth requirements of such applications and propose new schemes to efficiently offload such data.

Required skills: some background in computer graphics; proficiency in C++.

Desired skills: proficiency in computer graphics; experience with Android development.

47 SCI Computer Science Title: Statistical machine learning and deep learning for personalised medicine

Sub-Field: Machine learning, computational biology

Department: Computer Science

Professor in Charge: Harri Lähdesmäki ([email protected])

Academic Contact: Harri Lähdesmäki ([email protected])

Description: We are looking for summer internship students to work on probabilistic machine learning and Bayesian deep learning models for biomedical and health applications. Our on-going research projects involve several important clinical challenges, such as (i) personalized prediction of immunotherapy efficiency for cancer patients using e.g. modern single-cell data, (ii) time-series analysis of multi-omics data from biomedical studies, (iii) (semi-)supervised analysis of extremely large-scale heterogeneous health data from Finnish biobanks, and (iv) novel cancer diagnostic methods using cell-free DNA time-series. Your work would include familiarising yourself with one of these projects (based on your preference), contribute to developing statistical/deep learning methods, and apply them to exciting real-world data from our national or international collaborators. Applicants are expected to have good knowledge of machine learning/statistics, programming, and interest in developing/applying probabilistic methods for bioinformatics and biomedicine. Research work can be continued after the summer. For more information and relevant recent work, see (http://research.cs.aalto.fi/csb/publications) or contact Harri Lähdesmäki ([email protected]).

 

48 SCI Computer Science Title: Bayesian deep learning for designing bacterial genomes

Sub-Field: Machine learning, computational biology, bioinformatics

Department: Computer Science

Professor in Charge: Harri Lähdesmäki ([email protected])

Academic Contact: Harri Lähdesmäki ([email protected])

Description: Synthetic biology aims to manipulate and optimize bacterial species that are used in industrial and sustainable biotechnology. Advanced computational and bioinformatics methods have a central role in analysing large data sets from synthetic biology experiments and in designing bacterial proteins/genomes to achieve desired bioengineering goals. We are looking for summer interns to develop Bayesian deep generative models to analyse large-scale genetic data sets from high-throughput screening experiments and to optimize bacterial genomes to optimize protein expression in selected species relevant for industrial applications. Your work would include familiarising yourself with the state-of-the-art machine learning and deep learning methods, contribute to developing new statistical/deep learning techniques, and apply them to exciting real-world data from our collaborators in Europe. Applicants are expected to have good knowledge of machine learning/statistics, programming, and interest in developing/applying probabilistic methods for bioinformatics and synthetic biology. Research work can be continued after the summer. For more information and relevant recent work, see (http://research.cs.aalto.fi/csb/publications) or contact Harri Lähdesmäki ([email protected]).

 

49 SCI Computer Science Title: Bayesian deep learning

Sub-Field: Machine learning

Department: Computer Science

Professor in Charge: Harri Lähdesmäki ([email protected])

Academic Contact: Harri Lähdesmäki ([email protected])

Description: Bayesian inference methods for deep learning models promises to provide robust learning that are not sensitive to overfitting and provide reliable uncertainty estimates. Our recent work include recently proposed deep/nonparametric differential equation models make it possible to learn arbitrary continuous-time dynamics from data without any prior knowledge. These models can also be used to implement state-of-the-art deep learning methods in the context of deep Gaussian processes or neural networks. These high-capacity continuous-time models can, however, suffer from over-fitting. Building on our recent results, your work involves further developing the models and implementing Bayesian inference methods (MCMC, variational inference) for robust inference. The work will include non-parametric probabilistic modelling, deep continuous-time models, deep Gaussian processes, and/or deep generative models (based on your preference). The goal of the summer internship is to contribute to development of these models and to implement these methods in e.g. Pytorch. The project requires good knowledge of machine learning, mathematics, statistics, and programming. Research work can be continued after the summer. For more information and relevant recent work, see (http://research.cs.aalto.fi/csb/publications) or contact Harri Lähdesmäki ([email protected]).

31

SCI

Physics

Title: Coarsening and flow of foams

Sub-Field:

Department: Applied Physics

Professor in Charge: Mikko Alava ([email protected])

Academic Contact: Antti Puisto ([email protected])

Description: Foams coarsen i.e. change their structure with time. This is a complex process involving several microscopic mechanisms forcing the topology of the foam to change accordingly. Since, external forcing influences the foam structure, the coarsening in flowing foam systems is expected to deviate heavily from that in static foams. Here, we attack this problem by 3D simulations of foams using a novel GPU- based simulation code. The candidates should have an interest in soft matter physics and/or computational physics.

32

SCI

Physics

Title: Antiferromagnetic superconductivity in a twisted van der Waals multilayer

Sub-Field: Theoretical physics, Strongly correlated physics, Magnetism and superconductivity

Department: Applied Physics

Professor in Charge: Jose Lado ([email protected])

Academic Contact:  Jose Lado ([email protected])

Description:
Summary

Superconductivity and magnetism are two paradigmatic electronic states found in solid-state materials and exemplify two remarkable cases of emergent phenomena driven by interactions in condensed matter physics. In conventional materials, magnetism and superconductivity are considered antagonists, and thus in those cases, the appearance of magnetism destroys superconductivity. However, in some theoretical scenarios, specific magnetic structures can be compatibles with a coexisting superconducting state. Twisted two-dimensional materials have demonstrated to be an outstanding platform to engineer controllable correlated electronic states, by providing a unique way to engineer artificial electronic states arising from a moire pattern. In particular, recent experiments have shown the emergence of ferromagnetic superconductivity in a twisted graphene multilayer, demonstrating the potential of these compounds to realize exotic superconducting states. The objective of this project is to theoretically study a twisted van der Waals material whose electronic structure can yield an antiferromagnetic state coexisting with intrinsic superconductivity. In particular, the student will show how a specifically designed twisted van der Waals system can simultaneously have magnetic and superconducting instabilities and link their occurrence to spectral and topological properties of the electronic structure. During the internship, the student will gain in-depth knowledge of twodimensional materials, superconductivity, and magnetism, providing him/her with an outstanding experience for future studies in theoretical physics and quantum materials.

Necessary skills
We look for a highly motivated BSc student in Physics, with a strong background in condensed matter physics, theoretical physics and computational physics. The project will combine several analytic and computational skills, and as a result we look for a student with experience in programming, ideally in the languages Julia or Python.

33

SCI

Physics

Title: Artificial Intelligence for Materials Physics

Sub-Field: chemistry, materials science, computer science

Department: Applied Physics

Professor in Charge: Patrick Rinke ([email protected])

Academic Contact: Milica Todorovic ([email protected])

Description: Machine learning of quantum mechanics has become an exciting new research field. We teamed up with computer scientists to develop an original artificial intelligence (AI) approach for materials physics: you will be using our Bayesian Optimzation for Structure Search (BOSS) AI tool.

In this summer project, you would develop and apply BOSS to better understand the fundamental interactions of matter that are related to industrial technologies. You could be using AI to design new advanced materials, guide experiments, or re-purpose materials for different functions and industrial technologies. You would be assisting experimental and computational groups at Aalto to make AI predictions based on their data. This project requires both programming skills and creative ideas on how to encode physics data efficiently for best AI results.

34

SCI

Physics

Title: Automated atomic assembly using low-temperature
scanning tunneling microscope (STM)

Sub-Field:

Department: Applied Physics

Professor in Charge: Peter Liljeroth ([email protected])

Academic Contact: Peter Liljeroth ([email protected])

Description: The project involves coding computer routines for controlling a low-temperature scanning tunneling microscope (STM) to build atomically well-defined structures on surfaces atom-byatom. Subsequently, these routines will be used in the laboratory. The tip of the STM can be used to push/pull individual atoms into the desired positions with atomic-scale precision. The goal of the project is to automatize this process using scripts written in Python, Matlab, or Labview that autonomously scan over the area of interest, identify the positions of the adsorbates that will be manipulated, design suitable paths for the manipulation and carry it out, while monitoring the success of the process in-situ.

Required skills: basic physics courses and programming skills in Python and Matlab. Knowledge
of Labview programming a plus.

35

SCI

Physics

Title: CVD growth of lateral transition metal dichalcogenide heterostructures

Sub-Field:

Department: Applied Physics

Professor in Charge: Peter Liljeroth ([email protected])

Academic Contact: Peter Liljeroth ([email protected])

Description: This project involves growing lateral heterostructures of transition metal dichalcogenides using chemical vapor deposition (CVD) and characterizing them using surface science methods (XPS, STM, AFM) and optical techniques (e.g. micro-Raman). The main part of the work is to optimize the CVD growth parameters for obtaining well-defined heterostructures. The applicant should have some experience on working a lab, prior experience on CVD or surface science tools is a plus.

36

SCI

Physics

Title: Electron transport in topological materials

Sub-Field: Theoretical physics

Department: Applied Physics

Professor in Charge: Pertti Hakonen ([email protected])

Academic Contact: Alexander Zyuzin ([email protected])

Description: The research project will focus on the theory of topological state of matter, including semimetals and insulators, which have emerged as a major new theme in the modern condensed matter physics. The primary aim is to explore electron transport phenomena in these novel materials. For a quick review please see,

https://en.wikipedia.org/wiki/Weyl_semimetal .
https://en.wikipedia.org/wiki/Topological_insulator.

Minimum necessary skills include taken courses on quantum mechanics and statistical physics. The student will get acquainted with the diagrammatic Green function technique and the elements of topology in condensed matter physics.  For more details contact Alexander Zyuzin ([email protected]).

Two positions are available.

37

SCI

Physics

Title: Entanglement detection in Dirac materials

Sub-Field: Condensed Matter Theory

Department: Applied Physics

Professor in Charge: Christian Flindt ([email protected])

Academic Contact: Pablo Burset ([email protected])

Description: Future quantum technologies that exploit the most fundamental principles of Quantum Mechanics like discreteness, coherence, and entanglement could lead to revolutionary changes in society and industry. In the Quantum Transport Group we combine all these counter-intuitive ideas to theoretically design solid-state devices that generate on-demand entangled pairs of electrons and work in the regime of a single or a few particles.

We use analytical tools to describe electronic quantum transport in nano-scale devices formed by superconductors and novel materials like graphene and topological insulators. In these seemingly unrelated materials, low-energy electronic excitations are described by a relativistic Dirac equation; thus importing ideas from particle physics into materials science. When Dirac materials are combined with superconductors, a Majorana state —a particle that is its own anti-particle— can arise. Majorana states can be interpreted as “half of a Dirac fermion” and are very promising candidates for especially robust quantum computation.

During this project, you will learn the basics of scattering theory and quantum transport in the presence of superconducting correlations and apply them to analyze the transport properties of a superconducting junction between two normal state electrodes (see figure). You will also familiarize yourself with the concepts of quantum entanglement and topology in condensed matter systems. Additionally, you will help us develop new theoretical tools for the study of quantum devices in the almost unexplored regime of a single or a few particles.

38

SCI

Physics

Title: Generation and detection of entanglement in dynamic Cooper pair splitters

Sub-Field: Theoretical physics
Quantum transport
Quantum information

Department: Applied Physics

Professor in Charge: Christian Flindt, [email protected]

Academic Contact: Christian Flindt ([email protected]), Fredrik Brange ([email protected])

Description:
Summary

Generation and detection of entangled particles is a key ingredient in many quantum information applications, including quantum teleportation, super-dense coding and quantum cryptography.  Over the last two decades, Cooper pair splitters have emerged as a promising way of generating entangled electrons in nanoscale systems. In these systems, entangled pairs of electrons are extracted from a superconductor and spatially separated into normal metallic leads via, e.g., two single-level quantum dots. Aiming at on-demand production of entangled electrons, this project will focus on a dynamically driven Cooper pair splitter where the energy levels of the quantum dots are tuned in and out of resonance with the superconductor, ideally resulting in a well-controlled flow of entangled pairs of electrons. To fully describe this flow of electrons, we will employ the density matrix formalism and use a Lindblad-like master equation. We will extract the full counting statistics, allowing us to obtain both the currents, the noise and the higher order cumulants of the transport statistics. We will investigate how the entanglement is manifested in these quantities and develop an experimental scheme to verify (and quantify) the entanglement of the emitted particles by using entanglement witnesses, a general method for detecting entanglement. The student will gain in-depth knowledge in how to describe transport in quantum systems, providing him/her with a solid background for future studies and research in theoretical physics, quantum transport and quantum information.

Necessary skills
We look for a highly motivated student in theoretical physics (or related areas), with a strong academic background in quantum physics. The project will mainly deal with analytic derivations and calculations, but may also involve numerical computations, such as Monte Carlo simulations. Good skills in programming is thus a merit, but not a strict requirement.

39

SCI

Physics

Title: Interaction-driven frustrated magnetism at the surface of
topological semimetals.

Sub-Field: Theoretical physics, Topology in condensed matter physics, Strongly correlated physics

Department: Applied Physics

Professor in Charge: Jose Lado ([email protected])

Academic Contact:  Jose Lado ([email protected])

Description:
Summary

Topological semimetals are exotic electronic states showing non-trivial topological properties, that are realized in certain three-dimensional bulk compounds. Materials hosting those states are being highly pursued for their potential to realize exotic physical phenomena such as parity and chiral anomalies, and for providing promising platforms for low-consumption electronic devices. Paradigmatic examples of these materials are Weyl semimetals, nodal line semimetals or triple point metals, and they are characterized for having protected surface modes stemming from topological non-trivial bulk invariants. In this line, recent theoretical and experimental advances have shown a plethora of elusive phenomena in the bulk of these systems, and a variety of exotic signatures have been identified in their surface states.

The objective of this project is to explore the effect of strong electronic interactions in the surface states of topological semimetals. In particular, the student will show how the interplay of non-trivial topological properties and electronic interactions provides a powerful playground to engineer topological magnetic textures and spin liquid states in the surface of topological semimetals. During the internship, the student will gain in-depth knowledge of topological physics, interaction effects in condensed matter, and magnetism, providing him/her with an outstanding experience for future studies in theoretical physics and quantum materials.

Necessary skills
We look for a highly motivated BSc student in Physics, with a strong background in condensed matter physics, theoretical physics and computational physics. The project will combine several analytic and computational skills, and as a result we look for a student with experience in programming, ideally in the languages Julia or Python.

40

SCI

Physics

Title: Machine learning in statistical physics

Sub-Field:

Department: Applied Physics

Professor in Charge: Mikko Alava ([email protected])

Academic Contact: Mikko Alava ([email protected])

Description: The task is to utilize the recently developed machine learning and artificial intelligence algorithms in statistical physics problems. The previous topics include predicting catastrophic events and local yielding or automatically classifying the material properties. Here, the algorithms are adapted to similar existing experimental and numerical datasets. The candidate is expected to have some programming experience and keen interest in computational physics.

41

SCI

Physics

Title: Machine Learning Strategies for Scientific Data Analysis

Sub-Field:

Department: Applied Physics

Professor in Charge: Adam Foster ([email protected])

Academic Contact: Adam Foster ([email protected])

Description: Scientific data can be generated through physical simulations, experimental laboratories and observations from real-world problems. Compared to just a few years ago, the advancement of scientific instruments, digital sensors and computational resources as well as storage devices have created huge collections of scientific data. Unlike traditional statistical analysis, Machine Learning (ML) thrives on growing data sets. The more data fed into an ML system, the more it can learn and apply the results to higher quality predictions and new insights. In this project, we will investigate and implement ML methods (e.g., kernel regression, autoencoders, deep learning) for finding key variables influencing physical phenomena and materials properties. In particular, we will develop and exploit the wealth of materials data available (most of it generated in our research group), and use ML to discover new materials and phenomena linked to them. Examples within the SIN group (http://www.aalto.fi/physics-sin) include modelling catalysis, interpreting microscopy imaging, molecular self-assembly, identifying exotic quantum phenomena and predicting hydration structures.

The detailed applications and tasks will be tailored according to the background of successful candidates. Applicants should have a basic knowledge of physics, data analysis and statistics. Knowledge of Python would be highly beneficial.

42

SCI

Physics

Title: Magnetic foams

Sub-Field:

Department: Applied Physics

Professor in Charge: Mikko Alava ([email protected])

Academic Contact: Mikko Alava ([email protected])

Description: Here we study complex systems in the shape of magnetic foams. These are made of foams where magnetic nanoparticles have been mixed in. The intern is supposed to be interested in the physics of soft matter, and have skills or be interested in learning experimental physics and data analysis. The project is a collaboration between two groups at the department (Alava and ERC Grant winner Jaakko Timonen) and involves experimental studies of such foams. The project continues the successful topic of self-propelled particles in foams from summer 2019.

43

SCI

Physics

Title: Revealing criticality in quantum systems using tensor networks

Sub-Field: Quantum physics
Theoretical physics
Strongly correlated physics

Department: Applied Physics

Professor in Charge: Christian Flindt ([email protected]),
Jose Lado ([email protected])

Academic Contact: Christian Flindt ([email protected]),
Jose Lado ([email protected])

Description:
Summary

When many quantum systems couple together, a variety of collective behavior can arise, from the emergence of exotic quasiparticles to the realization of macroscopic quantum states. The transitions between these phases, known as quantum phase transitions, are zero temperature transitions in quantum systems hosting an infinite number of components. Investigations of quantum phase transitions represent one of the most fertile areas of quantum and condensed matter physics and constitute an exciting open problem in the physics of quantum many-body systems. In particular, the point marking the transition between different states, known as the critical point, is a potential source of exotic phenomena at the macroscale, with outstanding implications for quantum technologies and quantum materials science. However, the interest in understanding phase transitions is as significant as the challenges their general solution represents. In this project, we will investigate critical quantum properties of a paradigmatic quantum many-body system by combining ground-breaking theoretical advances in the field of criticality, that allow the characterization of critical points from the fluctuations in finite size systems, with a state-of-the-art computational method in many-body physics, the tensor network formalism. The student will gain in-depth expertise in computational methods based on tensor networks and in the field of criticality, providing him/her with an outstanding experience for future studies in theoretical physics, quantum technologies, and quantum materials.

Necessary skills
We look for a highly motivated student in theoretical physics (or related areas), with a strong background in quantum physics, statistical physics, and/or computational physics. The student will combine a novel theoretical approach with a versatile computational method. As such, we are looking for a student with a strong academic background in theoretical physics, and possibly with experience in a programming language such as Julia, C++, or Python.

44

SCI

Physics

Title: Superconducting qubit – mechanical hybrid quantum systems

Sub-Field: Experimental quantum physics

Department: Applied Physics

Professor in Charge: Mika A. Sillanpää  ([email protected])

Academic Contact: Mika A. Sillanpää  ([email protected])

Description: In the Quantum Nanomechanics research group (https://www.aalto.fi/en/department-of-applied-physics/quantum-nanomechanics) we work on nanomechanical systems in the quantum regime, and on superconducting quantum hybrid systems. Experimental work is carried out in the premises of Low Temperature Laboratory. The summer project involves design, fabrication and measurement of devices, and give an excellent overview of cutting-edge experimental research on an exciting topic with a strong relevance to quantum technologies. Quantum bits made with Josephson junctions are considered the most promising platform for realization of quantum computer. Besides this distant goal, superconducting qubits can be useful for exploring hybrid quantum systems, and testing quantum mechanics in nearly macroscopic systems.  To this end, we offer the following summer projects:

1. GHz phononic waveguides
highly promising scheme for coupling superconducting qubits to mechanical systems, and for exploring the quantum states of moving objects, is an overtone scheme using piezoelectric mechanical (BAW) modes. A longer-term goal is to couple these systems to propagating acoustic waves. To this end, in this work you will explore a basic scheme where surface waves are generated electrically, and detected as excitations of an overtone BAW resonator after travelling a waveguide. You will design, realize and measure this type of device.

2. Surface-acoustic wave resonators
Besides overtone GHz acoustic modes, surface waves offer an intriguing platform for coupling acoustic waves to superconducting qubits. In this project, you will simulate a surface acoustic wave resonator, fabricate a real device in the cleanroom on top of a piezoelectric substrate, and carry out basic characterization at room temperature.

45

SCI

Physics

Title: Thermodynamics close to a quantum phase transition

Sub-Field: Quantum physics
Theoretical physics

Department: Applied Physics

Professor in Charge: Christian Flindt ([email protected])

Academic Contact: Paul Menczel, [email protected]

Description: The quantum mechanical Rabi model consists of a single qubit interacting with a quantum harmonic oscillator [1]. It can be used to describe common experimental setups in modern research, such as a superconducting qubit inside a waveguide cavity. In the presence of dissipation due to losses in the cavity, the Rabi model undergoes a second order dissipative phase transition [2]. In this project, we will study the thermodynamic properties of the Rabi model and how they are affected by the phase transition using modern tools in theoretical physics.
 
The student should possess a solid understanding of basic quantum mechanics and statistical physics and, ideally, have some experience with writing code in the Python programming language. The student will become familiar with new concepts from the fields of quantum thermodynamics and quantum transport, such as quantum master equations or full counting statistics. The student will apply these concepts in order to perform analytical and numerical calculations of thermodynamic quantities in the Rabi model. The numerical calculations will be performed using the state-of-the-art open source framework “QuTiP”, a powerful tool for simulating open quantum mechanical systems.
 
References:
[1] Hwang et al., Phys. Rev. Lett. 115, 180404 (2015).
[2] Hwang et al., Phys. Rev. A 97, 013825 (2018).

  • Published:
  • Updated:
Share
URL copied!