Admissions

We offer the possibility to join world-class research groups either at Aalto University or University of Helsinki, with multiple interesting research projects to choose from. We organise two open calls for new doctoral students annually.

Our fully-funded doctoral student positions have gained a lot of international interest since their introduction in 2014. We appreciate applicants with diverse backgrounds and enthusiasm in computer science.

The quality of research and education in both HICT universities is excellent and our doctoral students are typically hired as full-time employees for the duration of their doctoral studies. The average duration of doctoral studies is four years in Finland.

You are most welcome to apply for HICT doctoral student positions! Our open positions are announced on this webpage.

Winter call 2022 – How to apply?

  1. Get familiar with the application process, eligibility requirements and compulsory attachments described on General HICT Call FAQ.
  2. Choose the projects from the list below.
  3. Fill in the application form and send it before deadline.

The call is open. The application period ends on February 6 at 11:59pm Finnish Time (UTC+2)

Helsinki ICT network: Doctoral student positions in computer science

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 80 professors and over 200 doctoral students, and the participating units graduate altogether more than 40 new doctors each year.

The participating units of HICT have currently funding available for qualified doctoral students. We offer an exciting opportunity to join world-class research groups, with over 20 research projects to choose from. The activities of HICT and the themes of open positions 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
     

If you wish to be considered as a new doctoral student in HICT you can apply to one or a number of doctoral student positions. We actively work to ensure our community’s diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community.

The online application form closes February 6, 2022  at 11:59pm Finnish time (23:59 EET, Eastern European Time UTC+2).

Our researchers are seeking doctoral candidates to fulfill positions for 29 projects. They are listed below grouped by primary research area.

How to Apply: Read through the list of available positions and select the topic(s) that you are interested in. Click on the link of the topic that you are interested in, select Apply and Create account. (Note: you will only have to create an account for your first application). If you are wanting to apply for multiple positions, use your log in information with the account that you just created and select “Use my latest application” after you have submitted the first application.

Projects in the HICT Winter call 2022

Algorithms and Machine Learning Research Area Projects

Life Science Informatics Research Projects

Networks, Networked Systems, and Services Research Projects

Software and Service Engineering and Systems Research Projects

User Centered and Creative Technologies Research Projects

Choose your topics below and fill in their application form.

Algorithms and Machine Learning Research Area Projects

AI Algorithms for Quantitative Biology

Supervisor: Professor Ville Mustonen (University of Helsinki) (Aalto University)

Research Area(s): Algorithms and Machine Learning (AML)

Biology is being transformed by the arrival of Big Data. As it stands, further biological insights are limited by the lack new bespoke AI algorithms that can be applied on these data. These algorithms must be theory aware of the respective scientific problem so as to be interpretable. Only this way will AI powered analyses lead to sustained scientific progress in biology and other natural sciences. Here we develop theory aware, interpretable, AI for automated growth law determination form massively parallel phenotyping data. The project builds a foundation towards AI guided microbial growth control.

The ideal candidate will have experience in machine learning methods e.g. reinforcement learning and symbolic regression. However, as our research is cross-disciplinary it is possible to contribute coming from several different fields. Thus, we welcome applications from exceptional candidates, with a highly quantitative background, from other fields.

For more information, please contact Prof. Ville Mustonen (v.mustonen@helsinki.fi).


AI Technologies for Interaction Prediction in Biomedicine

Supervisor: Juho Rousu (Aalto University)

Secondary Supervisors: Tero Aittokallio (FIMM), Tapio Pahikkala (University of Turku), Antti Airola (University of Turku)

Research Area(s): Algorithms and Machine Learning (AML), Life Science Informatics (LSI)

Several positions are open in a project with the aim of developing new AI techniques for predicting interactions of objects. Such problems are prevalent in society, found in numerous applications, such as various link prediction problems in social networks, recommendation systems, and healthcare.
The project will tackle several key challenges, including improving explainability, efficient use of resources, and reliability of the predictions. The technologies will be rigorously evaluated in practical applications in biomedicine such as predicting drug combination responses in cancer (Julkunen et al., Nature Communications, 2020). The positions are affiliated to a large multi-year research grant funded by Academy of Finland.

Other Supervisors of this project include Tero Aittokallio (FIMM), Tapio Pahikkala (University of Turku), Antti Airola (University of Turku)


Julkunen, H., Cichonska, A., Gautam, P., Szedmak, S., Douat, J., Pahikkala, T., Aittokallio, T. and Rousu, J., 2020. Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects. Nature communications, 11(1), pp.1-11. 


AI-Assisted Design

Supervisor: Antti Oulasvirta (Aalto University) as the primary with Samuel Kaski (Aalto University), Perttu Hämäläinen (Aalto University), or Arto Klami (University of Helsinki) as the secondary

Research Area(s): Algorithms and Machine Learning (AML), User Cenetered and Creative Technologies (UCCT)

The Finnish Center for AI (www.fcai.fi) is working on a new paradigm of AI-assisted design that aims to cooperate with designers by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers’ goals and then help them without being needlessly disruptive. We use generative user models to reason about designers’ goals, reasoning, and capabilities. We are looking for a PhD students to join our effort to develop AI-assisted design. Familiarity with one of the following is preferred: deep reinforcement learning, Bayesian inference, cooperative AI, computational cognitive modelling, and user modelling.

Example publications by the team

[1] https://arxiv.org/abs/2107.13074v1 

[2] https://dl.acm.org/doi/abs/10.1145/3290605.3300863 

[3] https://ieeexplore.ieee.org/abstract/document/9000519/    

[4] http://papers.nips.cc/paper/9299-machine-teaching-of-active-sequential-learners 

Supervision (candidates): Prof. Antti Oulasvirta (Aalto University) as the primary with Samuel Kaski (Aalto University), Perttu Hämäläinen (Aalto University), or Arto Klami (University of Helsinki) as the secondary


AI-Assisted Modelling in Economic

Supervisor: Otto Toivanen (Aalto), Sami Kaski (Aalto)

Research Area: Algorithms and Machine Learning

We are starting collaboration to address a few key questions in economics with new machine learning based approaches. Interesting topics include: 1. health economics: data-driven targeting of preventive treatments; 2. prior elicitation for economic models; 3. AI-assisted mechanism design and design of economic models. We welcome strong applicants with an interest in doing a PhD jointly supervised by a machine learning professor from the Finnish Centre for Artificial Intelligence FCAI and and an economics professor from Helsinki Graduate School of Economics GSE.

Keywords: Machine learning, AI-assisted modelling, economics


Bayesian Workflows for Iterative Model Building and Networks of Models

Supervision: Aki Vehtari (Aalto University)

Research Area: Algorithms and Machine Learning

Statistical analysis is critical when it comes to obtaining insights from data. Despite the practical success of iterative Bayesian statistical model building, it has been criticized to violate pure Bayesian theory and that we may end up with a different model had the data come out differently. In this project, we formalize and develop theory and diagnostics for iterative Bayesian model building. The practical workflow recommendations and diagnostics guide the modeller through the appropriate steps to ensure safe iterative model building, or indicate when the modeller is likely to be in the danger zone.

Sometimes the user wants also to build and compare models of different complexity or based on different assumptions.  Similar workflow ideas can be used to support also analysis of networks of models, making it easier to illustrate similarities and important differences. By making applied scientific research and data analysis more reliable and reproducible, our understanding of the world and decision-making will be improved. Related paper

https://arxiv.org/abs/2011.01808 and video https://www.youtube.com/watch?v=ppKpwtGy8KQ


Civic Agency in AI? Democratizing Algorithmic Services in the City (CAAI)

Supervisor: Nitin Sawhney, Professor of Practice, Department of Computer Science, Aalto University

Research Area: Algorithms and Machine Learning

The public sector is increasingly embracing algorithmic decision-making and data-centric infrastructures to offer innovative digital services to citizens. The CAAI project conducts research to assess wide ranging and conflicting perspectives and practices among experts, providers and citizens. The European Commission’s proposed Artificial Intelligence Act has raised vigorous deliberations regarding the implications of implementing this regulatory framework across the EU. The diverse and contested discourses among AI experts, regulators, public actors, and citizen advocates, offer a timely window of opportunity to critically examine their implications, while promoting citizen participation and civic agency in shaping the AI Act and its governance in Finland and the EU. In this project we examine discourses through the theoretical lens of Critical Discourse Analysis to gain insights on diverse values, narratives, and positions, while Natural Language Processing methods are used to contextually examine salient topics.

As part of this analysis the project will create an annotated textual corpus as an open access digital repository of rich discourses around the algorithmization of public services. Researchers will conduct in-depth case studies of public AI services in Finland to highlight the key practices and challenges for incorporating AI in the public sector, while ensuring trust, accountability and governance. We explore and evaluate citizen perspectives, agency, and imaginaries on digital citizenship and algorithmic literacy for democratization of public services to design frameworks for stakeholder participation in AI governance. Participatory Action Research will engage citizen and public sector actors in critical dialogue and participatory design of potential public AI services in Helsinki and other urban contexts. An interdisciplinary approach, combining social science, linguistics, design research, and ethical AI, allows us to critically assess the transformation of discourses, civic agency, and democratization of public AI services and design frameworks for stakeholder participation in AI governance.

Applicants must show a keen interest and background research in topics related to this project including AI ethics, digital citizenship, human computer interaction, participatory design, algorithmic literacy, AI governance, critical discourse analysis, and/or natural language processing.

Research Areas: AI Ethics, Algorithms, and Human Computer Interaction


Collaborative AI for AI-Assisted Decision Making

Supervisor: Sami Kaski (Aalto), Luigi Acerbi (University of Helsinki), other professors involved in the topic

Research Area: Algorithms and Machine Learning (AML)

We develop probabilistic modeling and inference techniques that take into account the down-the-line decision making task. A particularly interesting case is delayed-reward decision making where data has to be measured, at a cost, before making the decision. This problem occurs in designing the design-build-test-learn cycles which are ubiquitous in engineering systems, and experimental design in sciences and medicine. The solutions need Bayesian experimental design techniques able to work well with both simulators, measurement data and humans in the loop, who are both information sources and the final decision makers. Furthermore, we need automatic design of interventions (actions) for learning causal models partially from a combination of observational and interventional data.

We are looking for a probabilistic modeling researcher interested in developing the new methods, with options on applying the techniques to improve modeling in the FCAI’s Virtual Laboratories

Keywords: Sequential design of experiments, Bayesian experimental design, active learning

This project is a part of the Finnish Center For Artificial Intelligence (FCAI). To find out more please visit https://fcai.fi/


Deep Generative Modeling for 1) Precision Medicine and 2) Continuous-Time Dynamical Systems

Supervisor: Assoc. Prof. Harri Lähdesmäki (Aalto)

Research Area(s): Algorithms and Machine Learning (AML), Life Science Informatics (LSI)

Project-1:
We are looking for a doctoral student to develop novel probabilistic machine learning methods for large-scale health datasets from biobanks, clinical trials and/or single-cell sequencing experiments. This project develops novel deep generative modeling methods to (i) predict adverse drug effects using longitudinal/time-series data from large-scale biobanks and clinical trials, and to (ii) harmonize large-scale health data sets for AI-assisted decision making to revolutionize future clinical trials. Methodologically this project includes e.g. VAEs, GANs, Bayesian NNs, domain adaptation, Gaussian processes and causal analysis. The work will be done in collaboration with research groups from the Finnish Center for Artificial Intelligence, and the novel methods will be tested using unique real-world data sets from our collaborators in university hospitals and big pharma company.

Our recent work:
[1] http://proceedings.mlr.press/v130/ramchandran21b.html
[2] http://proceedings.mlr.press/v130/ramchandran21a.html
[3] https://arxiv.org/abs/2111.02019
[4] https://academic.oup.com/bioinformatics/article/37/13/1860/6104850

Project-2:
Recent machine learning breakthroughs include black-box modeling methods for differential equations, such as Gaussian process ODEs [1] and neural ODEs. These methods are particularly useful in learning arbitrary continuous-time dynamics from data, either directly in the data space [1] or in a latent space in case of very high-dimensional data [3]. We are looking for a doctoral student to join our current efforts to (i) develop efficient yet calibrated Bayesian methods for learning such black-box ODE models, (ii) develop neural ODEs to learn arbitrary dynamics of high-dimensional systems (e.g. in robotics, biology, physics or video applications) using a low-dimensional latent space representation, and (iii) further developing these methods for reinforcement learning and causal analysis.

Our recent work:
[1] http://proceedings.mlr.press/v80/heinonen18a.html
[2] http://proceedings.mlr.press/v89/hegde19a.html
[3] https://papers.nips.cc/paper/9497-ode2vae-deep-generative-second-order-odes-with-bayesian-neural-networks
[4] https://openreview.net/forum?id=aUX5Plaq7Oy
[5] https://proceedings.mlr.press/v139/yildiz21a.html
[6] http://arxiv.org/abs/2106.10905

For more information: https://research.cs.aalto.fi/csb/publications.shtml
Contact: harri.lahdesmaki@aalto.fi

Please list which project you are applying for in your application.


Deep Learning for Large-scale Historical Text Data

Supervisor: Rohit Babbar (Aalto)

Research Area: Algorithms and Machine Learning

In this project, we aim to develop algorithms and deep learning frameworks for studying large-scale historical textual data in the presence of input and labeling noise. There are many facets to the project including (i) finetuning and testing language models on large-scale corpora, (ii) building supervised and un-supervised learning methods for discourse detection in noisy text data, and (iii) fine-grained category detection in the setting of extremely large number of outputs. 

Given the problem scale consisting of tens of GBs of raw-text, efficiently leveraging the HPC infrastructure such as GPU computing (provided by CSC) remains central to the project. Apart from the core machine learning and NLP tasks, the project will also involve interaction with researchers in Humanities.

Looking for : Self-motivated PhD candidates who are comfortable with deep learning frameworks such as Pytorch

(https://sites.google.com/site/rohitbabbar/Home)


Deep Representation Learning – Foundations and New Directions

Supervisor: Prof. Vikas Garg (Aalto)

Research Area(s): Algorithms and Machine Learning (AML), Life Science Informatics (LSI)

Applications are invited for a PhD position and a Postdoctoral position in deep representation learning, broadly construed. Topics of particular interest include:

(1) Generative Models
(2) Graph Neural Networks
(3) Neural ODEs/PDEs/SDEs, Deep Equilibrium Models, Implicit Models
(4) Differential Geometry/Information Geometry/Algebraic Methods for Deep Learning
(5) Learning under limited data, distributional shift, and/or uncertainty
(6) Bayesian Methods, Probabilistic Graphical Models, & Approximate Inference
(7) Fair, diverse, and interpretable representations
(8) Off-policy reinforcement learning, inverse reinforcement learning, and causal reinforcement learning
(9) Multiagent systems and AI-assisted human-guided models
(10) Learning on the edge (i.e., learning under resource constraints)
(11) Applications in physics, computer vision, drug discovery, material design, synthetic biology, quantum chemistry, etc.
(12) Quantum Machine Learning for structured spaces

Representative publications:

(1) John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola. Generative Models for Protein Design. NeurIPS (2019).
(2) Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola. Generalization and Representational Limits of Graph Neural Networks. ICML (2020).
(3) Vikas Garg and Tommi Jaakkola. Solving graph compression via Optimal Transport. NeurIPS (2019).
(4) Vikas Garg, Lin Xiao, and Ofer Dekel. Learning small predictors. NeurIPS (2018).
(5) Vikas Garg, Cynthia Rudin, and Tommi Jaakkola. CRAFT: Cluster-specific assorted feature selection. AISTATS (2016).
(6) Vikas Garg, Adam Kalai, Katrina Ligett, and Steven Wu. Probably approximately correct domain generalization. AISTATS (2021).
(7) Vikas Garg and Tommi Jaakkola. Predicting Deliberative Outcomes. ICML (2020).


Developing Novel Symmetry-Learning Algorithms for out-of-Distribution Generalization

Supervisor: Stephane Deny, Aalto University

Research Area: Algorithms and Machine Learning (AML)

Traditional deep learning methods typically require very large training datasets, and they only generalize well to data coming from the very same distribution as the training set. These two requirements make deep learning methods difficult to apply out-of-the-box to many industry problems where the training data is limited, and where the use-case data characteristics might not be represented in the training set. Recently, alternative deep learning architectures called equivariant neural networks have been proposed to tackle these issues. They consist in encoding prior knowledge of the symmetries of a problem into the architecture of a network. Applied to small medical imaging datasets, these methods are currently state-of-the-art and outperform traditional deep networks. However, these methods have been developed to build invariance to well-known symmetries found in images (e.g., translation, scale, rotation), and are therefore not directly applicable to other types of data, for which the specific symmetries of the problems are unknown (e.g., biomedical data). In this project, we will build upon my recent work (Bouchacourt et al., 2021: https://ai.facebook.com/blog/building-ai-that-can-understand-variation-in-the-world-around-us) to develop novel algorithms able to learn the symmetries of a problem from the data itself. The expected outcome is to learn data-efficient representations that can generalize to data characteristics never seen during training.

About me:  I am a new assistant professor in the Department of Neuroscience and Biomedical Engineering and the Department of Computer Science at Aalto University. During my PhD (Vision Institute of Paris) and postdocs (Stanford, Meta AI), I have worked on retinal interfaces for blind patients, neural data analysis and state-of-the-art methods for self-supervised learning and symmetry-learning (more: https://sites.google.com/view/stephanedeny/home)

Preferred skills: Some experience with a deep learning language such as PyTorch or Tensorflow. Some interest in the topic


Eco-Evolutionary Control Theory

Supervisor: Professor Ville Mustonen (University of Helsinki)

Research Area: Algorithms and Machine Learning (AML)

Evolution connects all living organisms and is the common thread across biology. Organisms evolve to better survive in their environments and to adapt to new challenges. This leads to complex dynamical scenarios, which are presently understood only in a limited way. Understanding evolution is one of the most intriguing scientific topics due to its ability to unify often seemingly disjoint fields of biology. Furthermore, quantitative understanding of evolution is a prerequisite to successfully combat pathogens, pests and loss of biodiversity. Predictability and control of evolving populations is an emerging topic of high scientific interest1,2 and vast translational potential in applications such as vaccine and therapy design. This project will develop eco-evolutionary control theory. For example, we will find out what are the key determinants of controllability of a multi-species bacterial community, and apply the results to therapy optimisation. These projects are at the interface of microbial evolution, statistical physics, and information theory. A highly quantitative background and a burning interest in evolution are required.

Supervision: Professor Ville Mustonen (University of Helsinki); This is a collaborative project with the Lässig Group at the University of Cologne and the Särkkä Group at Aalto University.

Keywords: stochastic optimal control, eco-evolutionary dynamics, evolutionary theory, drug resistance.

  1. Lässig M, Mustonen V, Walczak AM (2017) Predicting evolution. Nat Ecol Evol 1(3):1–9.
  2. Lässig M, Mustonen V, (2020) Eco-evolutionary control of pathogens. PNAS, 117 (33), 19694-19704

The ideal candidate will have experience in reinforcement learning, stochastic optimal control theory and evolutionary theory. However, as our research is cross-disciplinary it is possible to contribute coming from several different fields. Thus, we welcome applications from exceptional candidates, with a highly quantitative background, from other fields.

For more information, please contact Prof. Ville Mustonen (v.mustonen@helsinki.fi).


Explainable AI for Systems Biomedicine

Supervisor: Juho Rousu (Aalto University)

Secondary Supervisor(s): Sahely Bhadra (IIT Palakkad, India), Karthik Raman (IIT Madras, India)

Research Area(s): Algorithms and Machine Learning (AML) ; Life Science Informatics (LSI)

In this project, we aim to develop AI technologies to help knowledge discovery in big molecular biology data, with applications to improve diagnoses and personalized treatments of complex diseases.  We will extend our recently developed technology for learning sparse non-linear models, to the analysis of multi-view network data (Huusari et al., 2021), and develop a capability of embedding prior knowledge for explainability. In applications, we will focus on the analysis of metagenomic data arising from the human gut microbiome  (Ravikrishnan and Raman, 2021), which is known to have a significant role in many diseases such as irritable bowel syndrome and diabetes. The results are expected to give new knowledge on the role of the gut microbiome in these diseases, explained in terms of key metabolic signatures, and thus enable improved diagnosis and treatment. In machine learning, the methods extend the state of the art in learning from complex multi-view and network data.

Other supervisors of this project are:

Sahely Bhadra (IIT Palakkad, India), Karthik Raman (IIT Madras, India)


Huusari, R., Bhadra, S., Capponi, C., Kadri, H. and Rousu, J., 2021. Learning primal-dual sparse kernel machines. arXiv preprint arXiv:2108.12199.
Ravikrishnan, A. and Raman, K., 2021. Unraveling microbial interactions in the gut microbiome. bioRxiv.


Learning with probabilistic principles

Supervisor: Arno Solin (Aalto)

Research Area: Algorithms and Machine Learning (AML)

We are looking for exceptional and highly motivated doctoral students to work on algorithms and methods in combining probabilistic methods with deep learning, real-time inference, and dynamical models. This project has direction relating to various topics in this space: Bayesian deep learning, computer vision, generative models, stochastic differential equations, meta-learning, Gaussian and neural processes, and normalizing flows. These areas also summarize the research interests in the supervisor’s group.

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. Successful candidates are expected to have completed a Masters’s degree and have familiarity with machine learning and statistics.

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


Lifecycle Support of Machine Learning Applications

Supervisor: Prof Jukka K Nurminen (Helsinki) jukka.k.nurminen@helsinki.fi

Research Area(s): Algorithms and Machine Learning (AML); Software and Service Engineering and Systems (SSES)

The project focuses on MLOps, the DevOps type of practices applied to machine learning systems. The essence is to develop methods and techniques for all lifecycle activities, not just for the initial development of a machine learning model. The work is part of the IML4E ITEA-funded project (https://iml4e.org/), which started recently.

We are looking for a PhD student with an interest in scientifically approaching the challenges of developing, deploying, and operating machine learning systems. An ideal candidate would have good competence in both machine learning and software engineering. The detailed work will be refined with other research partners and with the background of the candidate.

In our team, we are running multiple projects in the area of AI practice.  Collaboration between the different projects and between the industrial participants is important.


Machine Learning for Collaborative AI

Supervisor: Sami Kaski (Aalto), Collaborators in Alan Turing Institute, TU Delft, Prof. Antti Oulasvirta (Aalto University)

Research Area: Algorithms and Machine Learning (AML)

We study how to build collaborative agents (the AI) which are able to help another agent (the user) perform a task. 

A prime goal for FCAI’s research is to develop a new form of AI that can better work with people and assist them in everyday tasks. This can be seen as a probabilistic modeling task which requires data-efficient inference on multi-agent models, and some prior knowledge from cognitive science. We are now looking for an outstanding machine learning researcher who wants to develop with us the theory and inference methods for this new task. This will involve multi-agent modeling, POMDPs and reinforcement learning, and inverse reinforcement learning.

A sample previous paper: Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski (2019). Machine Teaching of Active Sequential Learners Conference on Neural Information Processing Systems, NeurIPS 2019

Keywords: Collaborative AI, inverse reinforcement learning, reinforcement learning, computational cognitive modeling, interactive AI, Multi-agent modeling

This project is a part of the Finnish Center For Artificial Intelligence (FCAI). To find out more please visit https://fcai.fi/


Machine Learning for Human-AI Collaboration

Supervisors: Tuukka Ruotsalo (Helsinki)

Research area(s): Algorithms and Machine Learning (AML), User Centered and Creative Technologies (UCCT)

The Cognitive computing research group is seeking 1-2 PhD candidates to conduct research in Machine learning for Human-AI collaboration. The candidate will work on machine learning methods that allow new types of man-machine interactions in which machines can learn directly from human behaviour and physiology. The methods will be developed and applied for physiological sensing and brain-computer interfacing data. The position provides an opportunity to contribute both to fundamental computer science and ground-breaking human-computer interaction research. The candidate will join world—class research environment and will be part of the Cognitive computing  research group (https://www.cs.helsinki.fi/group/intercom/). The starting date is negotiable, but the positions are filled when suitable candidates are found.

Requirements

The work involves research-related activities, including conducting theoretical and applied research, data analysis, writing research articles, participating in and presenting research at academic conferences, and teaching-related activities.The ideal candidate has a strong background in either machine learning or human-computer interaction. However, sole understanding of modern machine learning methods and good programming skills are required. Excellent written and oral communication skills in English are needed. Applicants should have an MSc in computer science or a related field.


Privacy-Preserving and Federated Learning

Supervisor: Prof. Antti Honkela (Helsinki), Prof. Samuel Kaski (Aalto) , Prof. Patrick Rinke (Aalto)

Research Area: Algorithms and Machine Learning (AML)

Many applications of machine learning suffer from limited training data availability because data holders cannot share their data. The aim of this project is to develop solutions to this fundamental problem through efficient privacy-preserving learning methods that allow securely combining data from multiple data holders under guarantees that data will not leak. Possible approaches include extending cross-silo federated learning into collaborative learning through personalisation of the models to each party, and developing methods for generating privacy-preserving synthetic data. The security and privacy will be guaranteed by a combination of differential privacy and secure multi-party computation.

In this project, you will join our group in developing new learning methods operating under these guarantees, and applying them to real-world problems. Collaboration opportunities will enable testing the methods on academic and industrial applications. A strong candidate will have a background in machine learning or a related field. Experience in differential privacy and/or secure multi-party computation is an asset.

Keywords: Differential privacy, federated learning, personalisation, synthetic data

This project is a part of the Finnish Center for Artificial Intelligence (FCAI). To find out more, please visit https://fcai.fi/


Probabilistic Modelling and Bayesian Machine Learning

Supervisors: Sami Kaski (Aalto)

Research Area(s): Algorithms and Machine Learning (AML)

Probabilistic modelling and Bayesian machine learning
I am looking fora doctoral student to join the Aalto Probabilistic Machine Learning Group, to work on new probabilistic models and inference techniques. I have exciting research topics available around the following areas, and I am also open to new suggestions: (1) simulator-based inference, for combining first-principles models with learning from data, (2) Bayesian deep learning, (3) Bayesian reinforcement learning and inverse reinforcement learning, (4) with multi-agent modelling, and (5) privacy-preserving machine learning and synthetic data generation. Can be theoretical or applied work or both; the group has excellent opportunities for collaboration with top-notch partners in multiple applications, including user interaction, health, genomics, and neuroscience. Links: http://research.cs.aalto.fi/pml


Reconstructing Crisis Narratives for Trustworthy Communication and Cooperative Agency

Supervisor: Nitin Sawhney, Professor of Practice, Department of Computer Science, Aalto University

Research Area: Algorithms and Machine Learning

Join a collaborative team of PhDs, Postdocs and academic researchers working on a project jointly conducted between Aalto University and the Finnish Institute for Health and Welfare (THL). The project seeks to analyze and reconstruct crisis narratives using mixed-methods, combining qualitative research for narrative inquiry with computational data analytics of crisis discourses in news and social media to understand global pandemics. Candidates will work at the intersection of Human-Computer Interaction (HCI), design research, computational social sciences, and public health for critical societal impact. We expect the candidates to have backgrounds in computer science and/or the social sciences.

Potential duties and tasks may include the following (to be conducted as part of the research team):
1.     Examining the narratives emerging in crisis-related communication using qualitative research methods across various data sources including organization communications, news/media coverage, and social media exchange among diverse publics.
2.     Automating content analysis for narrative work using suitable machine learning techniques for Natural Language Processing (NLP) such as Conversation Analysis, Content Classification, and/or Sentiment Analysis. This includes collecting and curating datasets, devising suitable methodologies, setting up the research infrastructure and tools, and a pipeline for data extraction, analysis and validation.
3.     Representing and visualizing crisis narratives to support understanding and collaborative sensemaking among key stakeholders and diverse publics. These tools should support browsing, searching, and exploring information for selected crisis themes and narratives. Work in this area includes not only developing prototypes of visualizations, but also conducting design research, user experience (UX) evaluation, and pilot assessment of such tools.

The candidate is not expected to master all these domains, but work closely with a multi-disciplinary research team to lead design and development efforts, while learning and contributing to ongoing work in specific research areas of interest.


Research Areas: Computational Social Science, Machine Learning, Human-Computer Interaction (HCI)


Virtual Atmospheric Laboratory

Supervisor: Kai Puolamaki (Helsinki), Patrick Rinke (Aalto), Hanna Vehkamäki (UH), Theo Kurtén (UH)

Research Area: Algorithms and Machine Learning (AML)

We are looking for postgraduate students and postdoctoral researchers to build Virtual Atmospheric Laboratory (VILMA). The objective of VILMA is to model atmospheric molecular level processes efficiently and to understand the underlying mechanisms and causal connections. VILMA will combine first-principles quantum chemical and other simulations and probabilistic machine learning/artificial intelligence (ML/AI) models with interactive visualization.

Examples of fundamental ML/AI topics are: probabilistic emulator / predictive regression models for atmospheric processes (Lange 2021; Lumiaro 2021), randomization methods for interactive visual data exploration (Puolamäki 2020), advanced statistical methods for ML/AI (Savvides 2019), explainable AI (Björklund 2019), and Bayesian optimization (Todorovic 2019). 

You will work in multidisciplinary team of computer and atmospheric scientists. You should have basic knowledge of ML and related mathematics. We will consider applicants with backgrounds in computer science, atmospheric science, physics, and chemistry. Knowledge of natural sciences is considered an advantage, but specific prior knowledge of atmospheric processes is not required.

References:

More information: https://wiki.helsinki.fi/display/VILMA 

This project is a part of the Finnish Center for Artificial Intelligence (FCAI). To find out more, please visit https://fcai.fi/


Virtual Laboratories: Drug Design

Supervisor: Prof. Vikas Garg (Aalto), Prof. Samuel Kaski (Aalto University), Markus Heinonen (Aalto University)

Research Area: Algorithms and Machine Learning (AML)

We develop modeling methods for drug design, both generative models of the drug molecules and their effects, and collaborative AI methods for assisting the drug designers in their task. The idea is to help experts steer the modeling system towards their design goals, while eliciting their prior knowledge to improve the models of the drugs. This is difficult because the goals may be tacit, uncertain and evolving.

We work with leading pharma companies and academic groups in Europe, USA, and Canada. Key methods we will need: probabilistic modeling and Bayesian inference, multi-agent modeling, sequential experimental design, POMDPs, reinforcement learning and inverse reinforcement learning. We expect applicants to master some of these, or be exceptionally eager and quick learners.

Keywords: Drug design, generative modeling, human-in-the loop machine learning

This project is a part of the Finnish Center for Artificial Intelligence (FCAI). To find out more, please visit https://fcai.fi/.


Life Science Informatics Research Projects

Compressed Indexing for Pangenomics and Genomic Epidemiology

Supervisor: Simon J. Puglisi, Associate Professor, University of Helsinki, Department of Computer Science

https://www.cs.helsinki.fi/u/puglisi/

Research Area(s): Life Science Informatics (LSI); Algorithms and Machine Learning (AML)

A pangenome is a catalog of all the genetic variation – single base changes, structural variants, regulatory regions and genes – found within a population or species. Pangenomes are revolutionizing biology, providing new insights into evolution and biodiversity. For example, recent breakthroughs in population genomics have demonstrated that mapping and understanding variation within a species (represented by its pangenome) is becoming essential for most high-impact applications, such as biomarker discovery, pathogen surveillance, modeling tumor evolution and prediction of treatment outcomes. However, searching and mining the pangenome of a species is a computational bottleneck in related bioinformatics pipelines. This project will develop the next generation of indexing data structures that allow fast and scalable search and update of pangenomes. The aim is to design memory-efficient compressed data structures that are simultaneously able to store and enable rapid search over the genomic data that makes up the pangenome, and to design accompanying index construction algorithms capable of scaling to terabytes of sequence data. The project draws on the world-leading expertise in compressed data structures of the PI’s group, as well as that of the wider Algorithmic Bioinformatics research cluster at University of Helsinki.


Machine Learning for Health (ML4H)

Supervisor: Prof. Pekka Marttinen (Aalto) pekka.marttinen@aalto.fi

Research Area(s): Life Science Informatics (LSI); Algorithms and Machine Learning (AML)

Recent years have witnessed accumulation of massive amounts of health data, enabling researchers to address a range questions such as: how to allocate healthcare resources fairly and efficiently, how to provide personalized guidance and treatment to users based on real-time data from wearable devices, or how to use genomic data to understand disease or antibiotic resistance. Central challenges in ML4H include integrating noisy data from heterogeneous data sources, going beyond correlation to learn about causality, interpreting the models, and assessing the uncertainty of predictions, to name a few. We tackle these by developing models and algorithms which leverage modern machine learning principles: Bayesian neural networks, deep latent variable models, interactive machine learning, attention, reinforcement learning, and natural language processing. Our ongoing interdisciplinary projects include: analysis of nationwide healthcare register data, mobile health, genomics, antibiotic resistance, and epidemiology. Successful applicants are expected to have outstanding skills in machine learning, statistics, applied mathematics, or a related field. The focus of the position may be tailored based on the applicant’s interests to either methodological or interdisciplinary research questions, and examples of our both kinds of recent research can be found in https://users.ics.aalto.fi/~pemartti/.


Machine Learning in Precision Oncology

Supervisor: Sampsa Hautaniemi (University of Helsinki)

Research Area(s): Life science informatics (LSI), Algorithms and Machine Learning (AML)

Cross-disciplinary research group of Prof. Sampsa Hautaniemi focuses on discovering causes for drug resistance in cancer patients and effective treatment options to overcome them. We have strong track-record in computational method development for data from patient samples (e.g., https://pubmed.ncbi.nlm.nih.gov/33720334/) and translating research results to clinical practice (e.g., https://ascopubs.org/doi/full/10.1200/PO.18.00343). We coordinate one of the largest ovarian cancer precision oncology projects in the world funded by EU Horizon 2020 (DECIDER; https://www.deciderproject.eu/). In DECIDER our focus is to discover effective treatment options for ovarian cancer patients who do not respond to chemotherapy anymore. More information and all our publications are available at our group website: https://www2.helsinki.fi/en/researchgroups/systems-biology-of-drug-resistance-in-cancer

We are looking for PhD students with BSc/MSc level education in data science, machine learning or mathematical modeling, and interest in applying computational methods to cancer research. Basic knowledge of cancer biology or genetics is a plus but not required. The possible PhD projects range from developing computationally effective methods for very large datasets to modeling tumor evolution and integration multi-omics cancer data.

The Hautaniemi group belongs to the Research Program in Systems Oncology (ONCOSYS; https://www.helsinki.fi/en/faculty-medicine/research/research-programs-unit/systems-oncology-oncosys). ONCOSYS consists of internationally acclaimed basic, translational and clinical researchers who share the vision of using cutting-edge measurement technology, real-world data and AI in cancer research in order to discover effective diagnostic, prognostic and therapeutic approaches. If you enjoy doing science in an interdisciplinary and collaborative environment, consider applying to us


Networks, Networked Systems, and Services Research Projects

Machine Learning Analytics in Edge-Cloud-HPC Continuum

Supervisor: Linh Truong (Aalto)

Research Area(s): Networks, Networked Systems, and Services (NNSS); Algorithms and Machine Learning (AML); Software and Service Engineering and Systems (SSES)

The Aalto Systems and Services Engineering Analytics (AaltoSEA) Group concentrates and consolidates research activities, resources, results, and collaborations on foundational principles, concepts and techniques for service engineering analytics of distributed software systems and services, big data applications, machine learning systems and IoT

We are looking for PhD students for IoT, Edge, and Cloud services engineering research, especially ML analytics across IoT, Edge, Cloud and HPC continuum (monitoring, analysis, observability, experiments,

explainability) to achieve robustness, reliability, resilience and elasticity for ML/big data systems. This research direction spans across networked systems and services, serving engineering and machine learning.

Further information about the current work can be found at:

https://rdsea.github.io.


Sustainable ICT

Supervisor: Professor Jukka Manner, Aalto

Research Area: Networks, Networked Systems, and Services (NNSS)

ICT has had tremendous effect on our world, bringing new kids of services and enhancing old ones. In terms of sustainability, ICT helps, for example, to reduce green house gas emissions in other sectors, digital solutions support environmental protection and adaptation to climate change. Yet, at the same time the use of ICT is growing at a huge pace. The traffic on the Internet and mobile networks grows year after year, and new and larger data centres are built all over the world. At the same time the performance of ICT hardware has increased extremely fast. As an example, a smart phone today has the same computing performance as the super computers in the 90’s. ICT services consume increasing amounts of energy and natural resources are needed for building new devices.

The goals of this project are to understand the reasons why the use of ICT is growing and what can be done to lower the direct impact of the ICT sector on our environment. Growth of ICT can be attributed to new services but also to how services are built, and how little optimisations are done to make these digital services more resource aware.


Software and Service Engineering and Systems Research Projects

Efficient Algorithms on Multi-Model Databases

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

Research Area: Software and Service Engineering and Systems (SSES)

Databases play an important role in our world today. But one of the greatest challenges in current database systems is the “Variety”of the data. Multi-model databases have emerged to address this challenge by supporting multiple data models against a single, integrated backend. Unfortunately, the existing principles and research ideas of multi-model data management are so scarce and far from perfect. This project will tackle this challenge from the fundamental problems to practical techniques. We will propose novel solutions for pressing problems on multi-model data management, including unified data storage abstraction, unified querying, and indexing techniques, relaxing consistency model, and fine-grained multi-model isolation. As long term contribution, this project supports building a next-generation platform to solve the “Variety” challenge of big data.


User Centered and Creative Technologies Research Projects

Usable and Accessible eHealth Services

Supervisor: Sari Kujala (Aalto)

Research Area: User Centered and Creative Technologies (UCCT)

eHealth services use emerging information technology to support wellbeing, health, and healthcare. Finland is a forerunner in the digitalization of healthcare with an increasing number of national eHealth services such as Omakanta, Omaolo, and Health Village. The goal is to encourage people to be active in taking care of their health. However, eHealth services are least used by persons who need them most and not everyone can access and use the services. In two research projects (www.digiin.fi and www.nordehealth.eu), we aim to 1) support the development of usable and acceptable eHealth services to people who are most vulnerable and maybe least interested in health and 2) benchmark national patient portals with access to electronic health in Finland, Sweden, Norway, and Estonia.

We are looking for a PhD student who is interested in the health field and wants to work on the  societally important research topic. Familiarity with the health field, user-centered design, and statistical analysis methods is preferred.

Keywords: eHealth, Human-Computer Interaction

How to Apply: Read through the list of available positions and select the topic(s) that you are interested in. Click on the link of the topic that you are interested in, select Apply and Create account. (Note: you will only have to create an account for your first application). If you are wanting to apply for multiple positions, use your log in information with the account that you just created and select “Use my latest application” after you have submitted the first application.