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Summer jobs (Research assistant positions) at the Department of Communications and Networking (Comnet)

Want to have top summer job? We provide you a dynamic working environment and a ringside seat for the communication systems and networks research.
Robot hand

Comnet is a multi-disciplinary unit of research and higher education covering communications and networking technology, networking business and human aspects of communication and communications technology. In its area, Comnet is the largest unit in Finland. Comnet develops communications, information and teletraffic theory and conducts fundamental and applied experimental research in communications and networking technology. In shaping the Internet technology it is a significant force internationally.

Comnet has 11 Professors, 2 Professors of Practice, 140 employees and turnover of 9 M€.

Join us to learn new technology!

We offer you a dynamic working environment and a ringside seat for the communication systems and networks research. Summer jobs are also an ideal way of gaining work experience along with study credits. Salary basis: ELEC salary guidelines, based on number of credits in transcript.

Applications

should be sent to following email address: [email protected]

Email subject must be: Summer job 2020 nr. [Job number]

For example applications for 7. Renewing the Laboratory Course in Internet Technologies, subject would be: Summer job 2020 nr. 7.

The applications can be written in English, Finnish or Swedish.

Your application should include:

  • Short informal cover with justification why you are the person we are looking for,
  • CV
  • OODI transcript of records. 

If you apply for more than one position please send a separate email for each position. 

Closing dates:
- Positions 1, 2, 3, 4, 5, 6:                     January  17 th
, 2020,
by 3 p.m.

- Positions 7, 8, 9, 10:                            February 14th, 2020, by 3 p.m.

 

1. Addressing uncertainty in 2D gesture input (1 position)

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.

Contact person for further questions:
Luis A. Leiva (luis.leiva(a t)aalto.fi)

 

2. Data visualization technique for neighbourhood clustering (1 position)

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.

Contact person for further questions:
Niraj Ramesh Dayama (niraj.dayama(a t)aalto.fi)

 

3. Helping People to Learn with Artificial Teachers that Adapt to User’s Cognitive Characteristics (1 position)

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.

Contact person for further questions:
Aurélien Nioche (aurelien.nioche(a t)aalto.fi)

 

4. Interactive multi-objective optimization (1 position)

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).

Contact person for further questions: Morteza Shiripour (shiripour.morteza(a t)aalto.fi)

 

5. Robotic Simulation of Human Active Touch (1 position)

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 behaviours 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.

Contact person for further questions: Yi-Chi Liao (yi-chi.liao(a t)aalto.fi)

 

6. Self-Adapting User Interfaces

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).

Contact person for further questions:  Kashyap Todi (kashyap.todi(a t)aalto.fi)

 

7. Renewing the Laboratory Course in Internet Technologies (1-2 positions)

Student laboratory at Comnet provides opportunities students to have their hands dirty with real networking hardware. To get the best learning opportunities for our students, we both develop new tasks and improve existing ones. We would like you to have a good knowledge of networking technology (at least courses ELEC-E7310, ELEC-C7240 or equivalent knowledge). You should be able to work with real and virtual devices (routers, other network devices, Linux, FreeBSD, Windows) and produce good documentation. Previous experience of measurements (e.g., from laboratory courses ELEC-E7330) is also valued highly.

This is a great opportunity to show your skills and extend your knowledge in the area of networking!

Contact person for further questions:
Laboratory manager Markus Peuhkuri (markus.peuhkuri(a t)aalto.fi)

 

8. IoT, Blockchains and Smart Contracts (1 position)

Are you developer interested in blockchains, Internet-of-Things (IoT) and their real-world applications?

We are now looking for master’s thesis workers 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.

Contact person for further questions: Dmitrij Lagutin (dmitrij.lagutin(a t)aalto.fi)

9. Implementing and testing synchronization of massive MIMO antenna system (1 position)

In this summer work, we are looking for a candidate who helps to develop a synchronization system for massive MIMO frontend. The massive MIMO frontend will be implemented as FPGA base radio unit that receives outside clock. Your responsibility is to implement and the clock distribution system. In the SDR side, it requires programming of FPGA in the clock distribution unit side some electronics knowledge and the overall clock tuning system requires knowledge of signal estimation theory.

This summer job is a good possibility to learn basics of software defined radio and timing issues in massive MIMO system.

Contact person for further questions: Kalle Ruttik (kalle.ruttik(a t)aalto.fi)

10. Developing new teaching and research platform for millimetre wave laboratory work using robot hand and 60 GHz radio link (1 position)

In this summer work your will help to create new laboratory work platform for millimeter wave propagation features. In this work, we plan to use millimeter wave transmitter and receiver with directional antenna and measure signal reflection from different directions. For finding direction, we will user Arduino controlled robot hand. Good background for this work is given by Sähköpaja and Ohjelmistoradio courses.

Contact person for further questions: Kalle Ruttik (kalle.ruttik(a t)aalto.fi)

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