Postdoc, research fellow and doctoral researcher positions in machine learning and artificial intelligence

Come work with us to push the boundaries of knowledge by developing new machine learning principles and tools for AI-assisted decision-making, design and modeling.

Photo: Matti Ahlgren / Aalto University

Our research mission is to create new types of AI that are data-efficient, trustworthy, and understandable. We work towards this by building AI systems capable of helping their users make better decisions and design sustainable solutions across a range of tasks from health applications to autonomous traffic. Key areas of research include, but are not limited to, Bayesian modeling, differential privacy, reinforcement learning, robotics, simulator-based inference and user modeling (see the full list of topics below).

You will join a community of machine learning researchers who all make important contributions to our common agenda, providing each other new ideas, complementary methods, and attractive case studies. The research can be theoretical, applied, or both: We are launching multidisciplinary virtual laboratories – AI-assisted modeling environments, in which R&D tasks can be solved by building models and running virtual experiments. While the models and tasks of these laboratories are domain-specific, many AI-assisted tools that we create will be used across them.

Our international research environment provides you with a broad range of possibilities to collaborate with companies and academic partners, and it supports your growth as a researcher. All positions are negotiated on an individual basis and may include e.g. a relocation bonus, an independent travel budget or research software engineering support.

We want to offer everyone an inclusive and non-discriminating working environment. We warmly welcome qualified candidates from all backgrounds to apply and particularly encourage applications from women and other underrepresented groups in the field. 

The deadline for the postdoc/research fellow applications is January 30 and for the doctoral researcher applications February 6, 2022 (23:59, UTC+2).

Links to the application system are in the end of the page.

 
 

Who are we?

Finnish Center for Artificial Intelligence FCAI is a research community initiated by Aalto University, the University of Helsinki, and the Technical Research Centre of Finland VTT. We develop new types of AI that can work with humans in complex environments, and help renew industry and society.

FCAI is built on a long track record of pioneering machine learning research. Currently over 60 professors contribute to our research.

Our community organizes frequent seminars with prominent speakers and offers high-quality collaboration opportunities with other leading research networks and companies. For instance, FCAI hosts ELLIS Unit Helsinki and has a joint research center with NVIDIA.

Local and national computational services spearheaded by the EuroHPC supercomputer LUMI (200+ Pflops/s) provide our researchers with access to excellent computing facilities.

About Finland

Finland is a great place for living with or without family: it is a safe, politically stable, and well-organized Nordic society, where equality is highly valued and extensive social security supports people in all situations of life.

Finland's free high-quality education system is also internationally renowned. Finland is consistently ranked high in quality-of-life and has been listed asthe happiest country in the world for the fourth year running.

Find more information about living in Finland here and here.

Introduction: AI-Assisted Virtual Laboratories

We at FCAI believe AI will transform the way research and development work is carried out in a broad range of disciplines. This requires a coordinated effort in identifying the central tasks and challenges of the research process itself, and development of collaborative AI methods for assisting in them. FCAI is driving this in the form of the Virtual Laboratory concept, developing the basis of AI-assisted support for research carried out in laboratories combining automated physical measurements and computational simulations. We are building the foundations and the AI methods needed for this, focusing in particular on techniques that can be applied across multiple fields of science. In parallel, we are establishing the first pilot Virtual Laboratories, to both steer the research and provide highly visible demonstrators. To contribute to this transition, you can apply for different kinds of roles, and work either on the underlying principles and methods (Topics F0, F6–F8; or for specific techniques and problems choose F9–F16) or directly on a specific Virtual Laboratory (Topics F1–F5).

Topic F0: Virtual Laboratories assisted by collaborative AI: From Foundations to Practice

Many of the key elements in the AI-assisted research process, as carried out in a Virtual Laboratory, are common across disciplines. For instance, a common recurring task is the choice and design of the experiment to be carried out next, and for efficient AI-assistance we need AI that can understand the goals and motivations of the researcher.

We are looking for people interested in these fundamental questions related to Virtual Laboratories and AI-assisted research. You will identify commonalities to build the foundational principles, work on better computational methods for addressing recurring challenges while working in collaboration with others who focus on a specific Virtual Laboratory, and will work towards a concrete software environment that supports several example cases. The long-term goal is to make it easy for everyone to run domain-specific Virtual Laboratories.  

We are looking for people with different kinds of profiles, with demonstrated excellence and strong desire to contribute to (a) the foundational basis of collaborative AI-assistance, (b) the required probabilistic machine learning techniques, and/or (c) open-source scientific software.

  • Supervision: Professors Arto Klami (University of Helsinki), Samuel Kaski (Aalto University), other virtual laboratories

  • Keywords: Virtual laboratory, AI-assisted design

  • Level: Postdoctoral researcher or research fellow

Topic F1: Virtual Laboratories: Multi-level Simulation for Sustainable Autonomy

To study future sustainable mobility, FCAI is building Sustainable Mobility and Autonomous Systems Virtual Laboratory. The virtual laboratory will allow studying effects of autonomous traffic starting from control of individual vehicles, to their environmental effects such as pollution and noise, as well as their socio-economic effects. The virtual laboratory will integrate several simulators including an autonomous vehicle simulator, as well as other simulators modeling relevant phenomena.

A central challenge in the integration is the exchange of information between the individual simulation models with different parameterizations. We approach this as an AI challenge where parameters of all simulators are inferred jointly from pools of data for each model. This will require efficient data-driven inference procedures.

More information about the topic >>

  • Supervision: Profs. Laura Ruotsalainen (University of Helsinki), Ville Kyrki (Aalto University), potentially with other supervisors

  • Keywords: Multi-level simulation, sustainability, autonomous vehicles, simulator-based inference

  • Level: Postdoctoral researcher or research fellow

Topic F2: Virtual Laboratories: Closing Simulation - Real World Gap

To study future sustainable mobility, FCAI is building Sustainable Mobility and Autonomous Systems Virtual Laboratory. The virtual laboratory will allow studying effects of autonomous traffic starting from control of individual vehicles, to their environmental effects such as pollution and noise, as well as their socio-economic effects. The virtual laboratory will integrate several simulators including an autonomous vehicle simulator, as well as other simulators modeling relevant phenomena.

When relying on simulation models for data-driven analytics, a central issue is the reality gap, the difference between a simulation model and the real world. In practice, simulation parameters need to be inferred often from scarce real-world data. However, in addition to this calibration problem, the simulation model is unlikely to capture all real-world phenomena. Thus, addressing the sim-to-real problem requires also determining this residual gap in order to compensate for it. This problem requires data-efficient probabilistic methods that are simultaneously expressive.

More information about the topic >>

  • Supervision: Profs. Ville Kyrki (Aalto University), Laura Ruotsalainen (University of Helsinki), potentially with other supervisors

  • Keywords: Sim-to-real problem, reality gap, data efficiency, autonomous mobility

  • Level: Postdoctoral researcher or research fellow

Topic F3: Virtual Atmospheric Laboratory

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 

  • Supervision: Profs. Kai Puolamäki (University of Helsinki), Patrick Rinke (Aalto University), Hanna Vehkamäki (University of Helsinki), Theo Kurtén (University of Helsinki)

  • Keywords: Machine learning, probabilistic modeling, simulator-based inference, natural sciences

  • Level: Postdoctoral researcher, research fellow or doctoral researcher

Topic F4: Virtual Laboratories: Synthetic Psychologist

Theories in psychology are increasingly expressed as computational cognitive models that simulate human behavior. Such behavioral models are also becoming the basis for novel applications in areas such as human computer interaction, human-centric AI, computational psychiatry, and user modeling. As models account for more aspects of human behavior they increase in complexity. The Synthetic Psychologist Virtual Laboratory broadly aims to develop and apply methods that assist a researcher in dealing with complex and intractable cognitive models. For instance, by developing optimal experiment design methods to help with model selection and parameter inference, or by using likelihood-free methods with cognitive models. This virtual lab will also encourage avenues of research relevant to cognitive modeling and AI-assistance which can be pursued in collaboration with other FCAI teams and virtual laboratories. We are looking for excellent candidates who are excited by cognitive models, Bayesian methods, probabilistic machine learning, and in open-source software environments, in no order of preference. 

  • Supervision: Profs. Luigi Acerbi (University of Helsinki), Andrew Howes (University of Birmingham),  Samuel Kaski (Aalto University), Antti Oulasvirta (Aalto University)

  • Keywords: Virtual laboratory, Cognitive Science, Simulator models, AI-assisted modeling

  • Level: Postdoctoral researcher or research fellow

Topic F5: Virtual laboratories: Drug Design

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.

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

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

  • Level: Postdoctoral researcher, research fellow or doctoral researcher

Topic F6: AI-assisted design 

FCAI 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. In this call, FCAI is looking for a postdoctoral scholar or research fellow to join our effort to develop AI-assisted design. Suitable backgrounds include deep reinforcement learning, Bayesian inference, cooperative AI, computational cognitive modeling, and user modeling.

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: Profs. Antti Oulasvirta (Aalto University), Samuel Kaski (Aalto University), Perttu Hämäläinen (Aalto University)

  • Keywords: AI-assisted design, user modeling, cooperative AI

  • Level: Postdoctoral researcher, research fellow or doctoral researcher

Topic F7: AI-assisted modeling

We are working on the development of a platform for integrated and semi-automated Bayesian Workflow. We aim at improving the probabilistic programming experience and the outcomes of Bayesian modeling by providing assistance at different steps of model building, while including the modeller in the loop. This will redound in a more pleasant modeling experience, one that is also less prone to errors and bad practices, while helping users to include more domain-knowledge into the modeling process.

Our main expertise is in Bayesian machine learning, and we collaborate actively with other global leaders including DTU, Cambridge, and Columbia in NYC. We also contribute to open source software, including Stan, ArviZ and other libraries. In particular, we focus on 1) The development of high-quality verified software for probabilistic programming. 2) The development of computationally efficient and accurate model-agnostic Bayesian inference algorithms. 3) The theory and tools for understanding the modeling workflow as a whole, integrating model specification, validation, refinement, and visualization.

Examples of publications:

[1] Kallioinen, N., Paananen, T., Bürkner, P.-C, Vehtari, A. (2021). Detecting and diagnosing prior and likelihood sensitivity with power-scaling. [preprint](https://arxiv.org/abs/2107.14054)

[2] Säilynoja, T., Bürkner, P.-C, Vehtari, A. (2021). Graphical Test for Discrete Uniformity and its Applications in Goodness of Fit Evaluation and Multiple Sample Comparison. [preprint](https://arxiv.org/abs/2103.10522)

[3] Dhaka, A. K., Catalina, A., Welandawe, M., Andersen, M. R., Huggins, J., & Vehtari, A. (2021). Challenges and Opportunities in High Dimensional Variational Inference. Advances in Neural Information Processing Systems, 34 [https://proceedings.neurips.cc/paper/2021/hash/404dcc91b2aeaa7caa47487d1483e48a-Abstract.html].

[4] Gelman, A., Vehtari, A., Simpson, D., Margossian, C. C., Carpenter, B., Yao, Y., Kennedy, L., Gabry, J., Bürkner, P.-C. & Modrák, M. (2020). Bayesian workflow. [preprint](https://arxiv.org/abs/2011.01808)

  • Supervision: Aki Vehtari (Aalto University), Arto Klami (University of Helsinki)

  • Keywords: Probabilistic modeling, Bayesian inference, Bayesian workflow

  • Level: Postdoctoral researcher

Topic F8: Collaborative AI for AI-assisted decision making

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 (see Topics 0-5).

  • Supervision: Profs. Samuel Kaski (Aalto University), Luigi Acerbi (University of Helsinki), other professors involved in the topic

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

  • Level: Postdoctoral researcher, research fellow or doctoral researcher

Topic F9: Machine learning for collaborative AI

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

  • Supervision: Prof. Samuel Kaski (Aalto University), collaborators in Alan Turing Institute, TU Delft, Prof. Antti Oulasvirta (Aalto University)

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

  • Level: Postdoctoral researcher, research fellow or doctoral researcher

Topic F10: ELFI: Engine for Likelihood-free Inference

ELFI (elfi.ai) is a leading software platform for likelihood-free inference of interpretable simulator-based models. The inference engine is built in a modular fashion and contains the most popular likelihood-free inference paradigms, such as ABC and synthetic likelihood, but also more recent approaches based on classifiers and GP emulation for accelerated inference. We are looking for postdoctoral researchers and research fellows to spearhead development of the next-generation version of the inference engine supporting new inference methods, including the use of PyTorch and deep neural networks for amortized inference, and using ELFI in cutting-edge applications from multiple fields of science.  

  • Supervision: Profs. Jukka Corander (University of Helsinki), Luigi Acerbi (University of Helsinki)

  • Keywords: Machine learning, emulators, simulator-based inference

  • Level: Postdoctoral researcher or research fellow

Topic F11: Design of Maximally Autonomous Collaborative AI Systems

Designing AI-agents that perform sequential tasks for users is challenging, especially in cases where the underlying goal is difficult to specify. The more automatically the AI system can operate, the more it can help us - but if it has not understood what we want, we would not want the help. The problem is compounded when the user is unable to provide ideal demonstrations to the agent due to some constraints. Such scenarios arise in many robotic applications, where providing optimal demonstrations is not straightforward.

We develop methods that can infer the underlying goal of a task through minimal user interaction and feedback. This requires the use of Bayesian experimental design techniques in combination with inference methods and interactive learning.

We are looking for people with a strong background in probabilistic machine learning, in particular reinforcement learning. Prior experience on robotics applications is a plus though not necessary - the principles are broadly applicable beyond robotics.

  • Supervision: Profs. Ville Kyrki (Aalto University), Samuel Kaski (Aalto University)

  • Keywords: Probabilistic machine learning, reinforcement learning, Bayesian experimental design, user modeling

  • Level: Postdoctoral researcher

Topic F12: Societal aspects of AI

The development of AI and machine learning has created new opportunities to utilize data, but good governance requires a common understanding of data and methods as well as a grasp of the related complex socio-legal-technical issues. The goal of this project is to increase a holistic understanding of how data are generated, processed, analyzed, and presented, looking at it from the point of view of modern AI methodology, thereby providing a foundation for common understanding about the limitations and opportunities. This improved understanding will lead to better decision-making, more realistic expectations about the possibilities of data analytics, and sharper critical discourse on future dangers.

The project is a close collaboration between social scientists, legal scholars, cognitive and computer scientists, as well as central Finnish institutions that manage much of the data about the Finnish population. The goal of the project is to advance socially and ethically acceptable uses of social and health data in public decision-making.

An ideal candidate has a PhD in machine learning or a related technical field, and a strong passion for interdisciplinary research on the burning societal issues. The role of the candidate is to contribute to both machine learning research with top experts in the field as well as interdisciplinary research together with our strong group of collaborators.

  • Supervision: Profs. Pekka Marttinen (Aalto University), Petri Ylikoski (University of Helsinki)

  • Keywords: Uncertainty, bias, fairness, decision making, hidden assumptions, missing data

  • Level: Postdoctoral researcher or research fellow

Topic F13: Machine learning to integrate family structure into health trajectories across 7.1 million individuals

Finland is one of the most advanced countries in the world with regard to collecting and accessing genetic, health and socio-economic data for research purposes. The Finregistry (https://www.finregistry.fi/) and FinnGen (https://www.finngen.fi/en) projects have aggregated an unprecedented amount of genetic, health and socio-demographic information by leveraging the power of nation-wide registers including information on hospitalisation, drug purchases, surgical operations, education, job profession, familial pedigrees, among many others. Our team (https://www.dsgelab.org/) is composed of statisticians, biologists, computer scientists and medical doctors and it is interested in developing and applying statistical and machine learning approaches to combine disease trajectories and genetic information with the goal to improve public health interventions. We are looking for an exceptional post-doctoral candidate that is passionate about understanding why people get sick and how computational approaches can help to identify at-risk individuals. In particular, the candidate will work on an exciting project to integrate health trajectories of family members and relatives for identifying individuals that are at higher risk to develop certain common diseases.

The successful candidate should prove a solid understanding of longitudinal data analysis from a biostatistical and/or machine/deep learning (i.e. recurrent neural networks, transformer models) perspective. Understanding of epidemiological design and measures is considered a plus. 

This project will be carried out in collaboration with the Eric and Wendy Schmidt Center (https://www.broadinstitute.org/ewsc) which provides mobility opportunities between FIMM and the Broad institute and the possibility to directly collaborate with world-leading experts at the intersection of machine learning and health at the Broad Institute, MIT and Harvard community.

  • Supervision: Andrea Ganna (FIMM, University of Helsinki), co-supervision: Prof. Pekka Marttinen (Aalto University), Anthony Philippakis (Broad Institute)

  • Keywords: Health data science, machine learning, genetics

  • Level: Postdoctoral researcher

Topic F14: Computational Rationality

Computational rationality is an emerging integrative theory of intelligence in humans and machines [1] with applications in human-computer interaction, cooperative AI, and robotics. The  theory assumes that observable human behavior is generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself [2]. Implementations use deep reinforcement learning to approximate optimal policy within assumptions about cognitive architecture and their bounds. Cooperative AI systems can utilize such models to infer causes behind observable behavior and plan actions and interventions in settings like semiautonomous vehicles, game-level testing, AI-assisted design etc. FCAI researchers are at the forefront in developing computational rationality as a generative model of human behavior in interactive tasks (e.g., [3,4,5]) as well as suitable inference mechanisms [5]. We collaborate with University of Birmingham (Prof. Andrew Howes) and Université Pierre et Marie Curie (UPMC, CNRS) (Dr. Julien Gori, Dr. Gilles Bailly).  

In this call, we are looking for a talented postdoctoral scholar or research fellow to join our effort to develop computational theory as a model of human behavior. Suitable backgrounds include deep reinforcement learning, computational cognitive modeling, and reinforcement learning.

References:

[1] S. Gershman et al. Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science 2015.
[2] R. Lewis, A. Howes, S. Singh. Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Topics in Cognitive Science 2014.
[3] J. Jokinen et al. Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation. Proc. CHI'21, ACM Press.
[4] C. Gebhardt et al. Hierarchical Reinforcement Learning Explains Task Interleaving Behavior. Computational Brain & Behavior 2021.
[5] J. Takatalo et al. Predicting Game Difficulty and Churn Without Players. Proc. CHI Play 2020.
[6] A. Kangasrääsiö et al. Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation. Cognitive Science 2019.

  • Supervision: Profs. Antti Oulasvirta (Aalto University), Andrew Howes (University of Birmingham), Samuel Kaski (Aalto University), Arto Klami (University of Helsinki), Perttu Hämäläinen (Aalto University)

  • Keywords: Computational rationality, computational cognitive modeling, deep reinforcement learning

  • Level: Postdoctoral researcher or research fellow

Topic F15: Privacy-preserving and federated learning

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.

  • Supervision: Prof. Antti Honkela, Prof. Samuel Kaski, Prof. Patrick Rinke

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

  • Level: Postdoctoral researcher, research fellow or doctoral researcher

Topic F16: Reinforcement learning under uncertainty

We are looking for exceptional and highly motivated candidates to work in the interface of model-based reinforcement learning and Bayesian deep learning. One of the main challenges in model-based RL methods is how to choose actions such that you collect information that leads to learning a model that can be used to plan optimal behaviour for a specified task. This project is concerned with solving model-based RL by (i) quantifying the uncertainty by approximative inference methods, and (ii) using the uncertainty estimates to explore states with both high uncertainty and which may be important for solving the task. The goal is to experiment with methods on a Boston Dynamics Spot robot.

Successful candidates are expected to have previous experience in RL and knowledge of probabilistic methods in machine learning. This is a co-located position between Prof. Pajarinen’s Robot Learning Lab (https://rl.aalto.fi/) and Prof. Solin’s ML group (http://arno.solin.fi). See group pages for recent publications on the topics.

  • Supervision: Profs. Joni Pajarinen (Aalto University) and Arno Solin (Aalto University)

  • Keywords: Reinforcement learning, model-based RL, uncertainty quantification, Bayesian deep learning

  • Level: Postdoctoral researcher or research fellow

How to apply?

The deadline for the postdoc/research fellow applications is January 30 and for the doctoral researcher applications February 6, 2022 (23:59, UTC+2).

This call is administered together with the Helsinki Institute for Information Technology HIIT and the Helsinki Doctoral Education Network in Information and Communications Technology (HICT). You can find the details on how to apply as well as more positions below: