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Helsinki ICT Network: Positions for exceptional doctoral candidates

The Helsinki Doctoral Education Network in Information and Communications Technology (HICT) is a joint initiative by Aalto University and the University of Helsinki, the two leading universities within this area in Finland. The network involves at present over 60 professors and over 200 doctoral students, and the participating units graduate altogether more than 40 new doctors each year.

The quality of research and education in both HICT universities is world-class, and the education is practically free as there are no tuition fees for doctoral students in the Finnish university system. In terms of the living environment, Helsinki has been ranked as one of the world's top-10 most livable cities (Economist, 2017), and Finland is among the best countries in the world with respect to many quality of life indicators, including being the overall #1 country in human wellbeing. Helsinki is in the second place in the world’s startup city comparison (Valuer, 2018) and is also the Mobile Data Capital of the World (IEEE Spectrum, 2018).

The activities of HICT are structured along five research area specific tracks:

  • Algorithms and machine learning
  • Life science informatics
  • Networks, networked systems and services
  • Software and service engineering and systems
  • User centered and creative technologies

The participating units of HICT have currently funding available for exceptionally qualified doctoral students. We offer the possibility to join world-class research groups, with multiple interesting research projects to choose from. If you wish to be considered as a potential new doctoral student in HICT you can apply to one or a number of doctoral student positions.

We welcome applicants with diverse backgrounds, and qualified female candidates are explicitly encouraged to apply. For more information on the positions, see "http://www.hict.fi/spring2019".

The online application form closes January 31, 2019 at midnight Finnish time.

Spring call 2019 projects

Project 1: Query processing and optimization on Unified Database Management System

Supervisor: Prof. Jiaheng Lu (Department of Computer Science, University of Helsinki)

As more businesses realized that data is critical to making the best possible decisions, we see the continued growth of systems that support massive volume of non-relational or unstructured data. The research focus of this project is to develop novel algorithms and theories for a unified database management system to manage both well-structured data and NoSQL data. 

Link: http://udbms.cs.helsinki.fi/

 

Project 2:  Data-driven capacity and resource management in vehicular fog computing

Supervisor: Prof. Yu Xiao ([email protected]), (Department of Communications and Networking, Aalto University)

The aim of the project is to solve fundamental challenges of providing reliable and cost-effective fog computing for data-intensive, compute-intensive and latency-critical vehicular applications. The outcomes are expected to include 1) big data supported methodologies for forecasting the computing and communication capacity demanded by future vehicular applications in urban areas. 2) An intelligent platform that provides data-driven mathematical models, tested methods and execution environments for capacity and resource management in vehicular fog computing.

Link: http://mobilecloud.aalto.fi/

 

Project 3: Data-driven and model-based system design

Supervisor: Prof. Stavros Tripakis (Department of Computer Science, Aalto University)

We are looking for motivated students to work in the intersection of artificial intelligence, formal methods, and system design, in a project we call "data-driven and model-based design (DMD)" (see https://users.ics.aalto.fi/stavros/papers/icps2018.pdf). The project is broad and depending on the candidate's interests can be taken in different directions, for instance: how can formal techniques benefit artificial intelligence? Can we make learning-based systems (e.g., neural networks) more understandable, predictable, reliable? How can such systems benefit system design? Can we use machine-learning techniques for program verification or synthesis? Can we learn formal models from data? etc.

Link: https://users.ics.aalto.fi/stavros/

 

Project 4: Machine Learning for Health

Supervisor: Prof. Pekka Marttinen (Department of Computer Science, Aalto University)

Recent years have witnessed accumulation of massive amounts of health related data, enabling researchers to address problems such as: how to allocate health care resources fairly and efficiently, how to provide personalized guidance and treatment to a user based on time-series data from wearable self-monitoring devices, or how to use genomic data to understand disease or antibiotic resistance. Answering these questions requires new machine learning methodology to be developed. We have several interdisciplinary research projects ongoing, where the goal is to design new machine learning models and algorithms for applications in health and welfare, together with leading experts in the respective fields from Finland and abroad. Examples of applications include: analysis of electronic health records, mobile health, genomics, antibiotic resistance, epidemiology. Successful applicants are expected to have an outstanding record in machine learning, statistics, or a related field, in particular in one or more of the following topics: probabilistic machine learning, deep learning, bioinformatics, Bayesian modeling, causal learning, time-series modeling, likelihood-free inference.

 

Project 5: Human-guided data analysis

Supervisor: Prof. Kai Puolamäki (Department of Computer Science, University of Helsinki)

The exploratory data analysis group has open a doctoral student position. The topics of interest include the use of randomization and physical simulations to model user’s knowledge and to explore data sets, as well as to make supervised machine learning models - such as deep learning models - more trustworthy and interpretable. Part of the work will be done in collaboration with Institute for Atmospheric and Earth System Research (INAR). Please contact Prof. Kai Puolamäki at [email protected] for further information.

 

Project 6: Open doctoral student position in Prof. Antti Oulasvirta’s group – computational methods in HCI

Supervisor: Prof. Antti Oulasvirta (Aalto University, Department of Communications and Networking)

This ERC funded group is looking for PhD students interested in applications of computational methods in HCI. Background and interest in data science, machine learning, modeling, neurosciences, or cognitive science is required. PhD topic will be negotiable. On-going topics in 2018 include, but are not limited to: 1) Interaction techniques for collaborating with an artificial intelligent agent in complex scientific tasks; 2) modeling of input using control theory, neuromechanics; 3) reinforcement learning models of human-computer interaction; 4) computational models of emotion.

Link: http://userinterfaces.aalto.fi/

 

Project 7: Probabilistic machine learning

Supervisor: Prof. Samuel Kaski ([email protected]) (Department of Computer Science, Aalto University)

We are looking for a student eager to join the Aalto Probabilistic Machine Learning Group to develop new probabilistic models and inference techniques. Particularly promising thesis topics at the moment are Approximate Bayesian Computation (ABC) techniques for inference in simulator-based models, with deep surrogate models and learning the simulators with deep learning methods. But we have a few other equally interesting topics in probabilistic modelling and Bayesian inference, both theory and exciting applications. Contact me for more information!

Links: http://research.cs.aalto.fi/pml/, http://elfi.readthedocs.io

 

Project 8: Human-in-the-loop machine learning and human-AI collaboration

Supervisor: Prof. Samuel Kaski ([email protected]), (Department of Computer Science, Aalto University)

Humans are increasingly interacting with machine learning based adaptive systems, both at work and as consumers. How to optimally combine the strengths of humans and machines is one of the most interesting scientific questions at the moment. We are developing new approaches and applications for interactive human-in-the-loop machine learning and human-AI collaboration - key questions are for instance: how should the AI design its interaction with the human to be maximally useful, what kind of a model would it need to learn of the human for that, and how can it learn the model on-line during the interaction. This project lies at the intersection of machine learning, human-computer interaction, and cognitive science. Relevant machine learning methodologies include reinforcement learning, inference in simulator-based models, probabilistic modelling and programming, and deep learning.

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

 

Project 9: Privacy-preserving machine learning

Supervisor:  Prof. Samuel Kaski ([email protected]), (Department of Computer Science, Aalto University)

Using data in decision making would enable better decisions, but the need to preserve privacy constrains data availability - we have all seen demonstrations how an adversary can recover private information from data analysis results. We develop methods for learning from data such that we can give guarantees that privacy of the data is preserved, using a concept called differential privacy. We have recently introduced ways of doing the learning such that performance actually improves, in contrast to in alternative methods. A couple of “minor” unsolved problems still remain; come solve them with us!

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

 

Project 10: Probabilistic machine learning for precision medicine and data-driven healthcare

Supervisor:  Prof. Samuel Kaski ([email protected]), (Department of Computer Science, Aalto University)

We are looking for a student to join us in developing new probabilistic modelling and machine learning methods needed for genomics-based precision medicine, causal inference and predictive modelling based on clinical data. We combine the ability of modern flexible models to take into account nonlinearities and interactions, with the Bayesian approach which provides a consistent and flexible way to combine available structural information and uncertain observations. In this project, you will develop new probabilistic modeling, Bayesian inference and machine learning methods to make personalized predictions for treatment outcomes, taking into account available side information and structure in the data. The ultimate goal is to personalize medicine both for the patient and the doctor. You will have a chance to collaborate with the machine learning experts in the Finnish Center for Artificial Intelligence (FCAI), as well as with our international collaborators in the top research groups on machine learning and healthcare. Suitable candidates have either a strong background in machine learning and a keen interest to work with top-level medical collaborators to solve these profound medical problems, or strong background in computational biology or medicine, and a keen interest to develop new solutions by working with the probabilistic modelling researchers of the group.

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

 

Project 11: Bayesian deep learning

Supervisors:  Prof. Samuel Kaski ([email protected]), Dr Markus Heinonen ([email protected]), (Department of Computer Science, Aalto University)

We are looking for an eager student to join the Aalto Probabilistic Machine Learning group to develop state-of-the-art Bayesian deep learning techniques. Promising thesis topics include (1) flexible function spaces (deep processes for large-scale data such as images or videos), (2) deep generative models, (3) developing probabilistic neural networks, and (4) deep learning with dynamic models. The PML research group has active complementary research on related topics to support the PhD project.

We also have excellent opportunities for applying the techniques across a range of practical problems, for instance in biology, reinforcement learning or human interaction. We will utilise the latest techniques such as probabilistic programming, Tensorflow, GPU’s, etc. Requirements: strong background in math, statistics or computer science and eagerness to learn the rest.

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

 

Project 12: Open doctoral student position in data science with applications to cyber-security

Supervisor: Prof. Nikolaj Tatti (Department of Computer Science, University of Helsinki)

Experts in cyber-security domain have recognized the need for data science techniques in order to keep up with the other side [1]. I am looking for a PhD Student to develop new data mining techniques with strong applications in cyber-security. Potential topics include, but not limited to, graph mining, temporal series analysis, string and log event mining, anomaly detection, adversarial machine learning. The work involves developing new methodology, analyzing its theoretical aspects, implementing and testing new algorithms, and publishing and presenting discovered results. The PhD position is at University of Helsinki. Familiarity with optimization, statistics, and programming, as well as eagerness to learn and master new concepts, increases the chance of being accepted.

[1] https://twitter.com/gartner_events/status/874255227435831296?lang=en

 

Project 13: Quantifying, Detecting and Analyzing Incidents in IoT Big Data

Supervisor: Prof. Hong-Linh Truong (Department of Computer Science, Aalto University)

Incidents occur in various places: software systems, algorithms across layers and infrastructures, due to data and system errors. Current there is no systematic way to quantify, detect and analyze incidents in a holistic manner for the whole complex big data analytics. Detecting incidents will substantially improve performance, cost, and quality of the analytics result. Therefore, incident detection and analytics techniques and methods are extremely important for developers and companies that rely on cloud  for IoT big data analytics. In this topic, we have identified the following key research problems: 

  • We need to identify and classify potential incidents in big data analytics, related to data sources, data in transit, data in processing, big data system services or ML algorithms. To have a classification and means to manage them help us to determine relevant measurements and logs for detecting and determining incidents.
  • We need to provide mechanisms to capture and extract relevant information for understanding incidents. This requires different techniques to instrument code, evaluate quality and error in data, and interact with external monitoring systems. We must have suitable APIs for monitoring data and analysis tasks, and plug-ins for dealing with middleware handling data in data analytics.
  • We need to analyze and root cause of incidents within  big data analytics not just due to the error of data (data quality) but the various inter-dependent factors between V* properties of big data, data quality, abilities of ML algorithms, resources and services configuration.

Addressing these questions will lead to a comprehensive framework with novel, intelligent techniques and methods for monitoring, analysis and optimization big data systems and analytics processes.

Links:

Previous work: https://www.researchgate.net/publication/324170664_Characterizing_Incidents_in_Cloud-based_IoT_Data_Analytics

https://github.com/rdsea/bigdataincidentanalytics

 

Project 14: Scalable probabilistic machine learning

Supervisor: Prof. Arno Solin, (Department of Computer Science, Aalto University)

We are looking for exceptional and highly motivated doctoral students to work on algorithms and applications for real-time machine learning. Central topics and methods in this project include (but are not limited to) Gaussian processes, variational inference, autoencoders, uncertainty quantification, and differential equations (both ODEs and SDEs). Applications of interests are in online decision-making, sensor fusion, control (also linking to RL), and computer vision. The research is a continuation of this NeurIPS paper: https://youtu.be/myCvUT3XGPc (see also related work on supervisor's home page).

The work will be done in close collaboration with the supervisor and other members of the team at Aalto University. Doctoral students in the group are encouraged to make research or internship visits to collaborating universities/companies during the course of study. Successful candidates are expected to have completed a Masters degree and have familiarity with machine learning and statistics.

For more information and recent publications and pre-prints, see the research group home page at http://arno.solin.fi

 

Project 15: Doctoral student position in computer vision and machine learning

Supervisor: Prof. Juho Kannala (Department of Computer Science, Aalto University)

Computer vision is a rapidly developing field that is at the forefront of recent advances in artificial intelligence. Our group has broad research interests within computer vision. We are pursuing problems both in geometric computer vision (including topics such as visual SLAM, visual-inertial odometry, optical flow, image-based 3D modeling and localization) and in semantic computer vision (including topics such as object detection and recognition, and deep learning). We are looking for students interested in both basic research and applications of computer vision. Students with good programming skills and strong background in mathematics are especially encouraged to apply. The precise topics of the research will be chosen together with the students to match their personal interests.

Examples of our recent papers include: https://aaltovision.github.io/PIVO/https://aaltovision.github.io/pioneer/https://arxiv.org/abs/1808.04999 and https://arxiv.org/abs/1810.08393. For more papers and further information visit: https://users.aalto.fi/~kannalj1/

 

Project 16: Algorithms for the design of RNA nanostructures

Supervisor: Prof. Pekka Orponen, (Department of Computer Science, Aalto University)

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. For instance, our group recently demonstrated, together with a biochemistry team from Karolinska Institutet, a general technique for rendering almost arbitrary 3D wireframe designs into biomolecules folded from a single long DNA strand [2].

For several reasons, there is increasing interest in the DNA nanotechnology community to move from DNA to RNA as source material. This is however quite challenging, because RNA has remarkably much richer and less well understood folding kinetics than DNA. Thus a current focus of our group is to develop algorithmic methods and tools to support the task of designing complex 2D and 3D RNA nanostructures. Some preliminary results are presented in the summary [3], and the goal of the present project is to pursue this work towards more complex and robust designs. This entails e.g. optimising the constitutive RNA sequences according to several nontrivial criteria, as well as modelling and controlling the folding kinetics of the eventual nanostructures.

Research group webpage: https://research.cs.aalto.fi/nc/

Online materials:

[1] https://en.wikipedia.org/wiki/DNA_nanotechnology

[2] http://old.cs.aalto.fi/en/current/news/2015-07-23/

[3] http://research.cs.aalto.fi/nc/sources/EKOM18.pdf

 

Project 17: Algorithms for mining large-scale graphs

Supervisor: Prof. Aristides Gionis, (Department of Computer Science, Aalto University)

We are looking for highly-qualified and motivated doctoral students to work on algorithms for mining large-scale graphs. We are focusing on analyzing temporal graphs, signed networks, and heterogeneous networks that incorporate textual information. Topics of interest include motif discovery, dense-subgraph finding, computation of importance and centrality measures, graph summarization, influence propagation, and epidemics. The PhD position is in the Data Mining group of Aalto University. Successful applicants are expected to have completed successfully a Masters degree from a reputable international university, and have familiarity with graph mining, machine learning, and/or combinatorial optimization.

Data Mining group website: http://research.cs.aalto.fi/dmg/

 

Project 18: Algorithms for content analysis and distribution in social media

Supervisor: Prof. Aristides Gionis, (Department of Computer Science, Aalto University)

We are looking for highly-qualified and motivated doctoral students to work on algorithms for content analysis and distribution in social media. Topics of interest include analysis of social-media content, controversy and polarization in social networks, echo chambers, dissemination of news in social media, opinion-formation models, and algorithms for online content recommendation. The PhD position is in the Data Mining group of Aalto University. Successful applicants are expected to have completed successfully a Masters degree from a reputable international university, and have familiarity with graph mining, machine learning, and/or combinatorial optimization.

Data Mining group website: http://research.cs.aalto.fi/dmg/

 

Project 19: Robust and Assisted Bayesian Modeling Workflows

Supervisor: Prof. Aki Vehtari, (Department of Computer Science, Aalto University)

We will develop theory and methods for assessing the quality of Monte Carlo and variatonal inference methods, and develop tools for a principled and robust 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.

 

Project 20: Machine learning and differential privacy

Supervisor: Prof. Antti Honkela, (Department of Computer Science, University of Helsinki)

Differential privacy allows developing machine learning algorithms with strong privacy guarantees. In this project, you will join our group in developing new more accurate methods satisfying these guarantees. Our work covers both Bayesian machine learning and deep learning. The project combines theory and practice and requires a strong background in mathematics.

Link: http://www.helsinki.fi/~ahonkela/


Project 21:
Probabilistic deep learning for personalised medicine

Supervisor: Assoc. Prof. Harri Lähdesmäki, Department of Computer Science, Aalto University https://users.ics.aalto.fi/harrila/index.html

We are looking for a doctoral student to develop probabilistic machine learning and deep generative models for biomedical and health applications. Research projects involve several important clinical challenges, such as personalized prediction of immunotherapy efficiency for cancer patients using e.g. modern single-cell data, and time-series and semi-supervised analysis of extremely large-scale heterogeneous health data in Finnish biobanks. Work is carried out in collaboration with biomedical research groups in Finland and abroad. Applicants are expected to have strong background in probabilistic machine learning and programming, and interest in developing/applying probabilistic methods for bioinformatics and biomedicine. For more information and relevant recent publications, see (http://research.cs.aalto.fi/csb/publications) or contact Harri Lähdesmäki ([email protected]).

Link: http://research.cs.aalto.fi/csb/

 

Application process

The Spring call 2019 includes a number of positions in specific PhD/DSc research projects by several HICT professors and recruiters. If you wish to be considered as a potential new doctoral student in HICT, you can apply directly to the specific research projects.

An applicant can choose up to 10 projects in the application form (please, mention the project number(s) in the application form). Express your motivation towards the project(s) in your motivation letter (compulsory attachment). You do not have to write several motivation letters in case you apply for multiple projects.

The application form closes on Thursday 31st January at 23.59 (midnight) Finnish time, after which applications will be reviewed. Incomplete applications or applications arriving after the deadline will not be considered. Based on the results of the review, top candidates will be invited to interviews.

All the supervisors you indicate on your application form will be informed of your interest, and others also have access to your application documents. If your application is considered strong enough, given the limited resources and intense competition, you will be contacted for a skype interview in February 2019.

Compulsory attachments

Please submit your attachments as single pdf file containing (all documents in English):

  1. Letter of motivation (max. one page) Please describe your background and future plans, and in particular the reasons for selecting the project(s) (you can get more information on the projects and supervisors through their web pages). Try to make your motivation letter as convincing as possible, so that the potential supervisors get interested.
  2. A curriculum vitae and list of publications (with complete study and employment history, please see an example CV at Europass pages)
  3. A study transcript provided by the applicant's university that lists studies completed and grades achieved.
  4. A copy of the M.Sc. degree certificate. If the degree is still pending, then a plan for its completion must be provided.
  5. Contact details of possible referees. Please, provide names, positions, affiliations, and e-mail addresses of 2-3 senior academic people available for providing recommendation letters upon request from HICT. We will contact you and the recommenders separately afterwards, if and when recommendation letters are required. 

 

Interested?