Open Positions

We are hiring!

  • Postdoctoral (or doctoral student) position in Probabilistic Machine Learning (FCAI co-supervised).

Please see job postings below.

Our group is committed to principles of diversity, equality and inclusion. We encourage applications from women, racial and ethnic minorities, and other under-represented individuals.

Postdoc (or doctoral) position in Probabilistic Machine Learning, FCAI-funded

The Machine and Human Intelligence research group has a potential opening for a co-supervised postdoc position (or for an excellent PhD candidate) funded by the Finnish Center for Artificial Intelligence (FCAI). Projects in our group relate to sample-efficient and active-sampling approaches in probabilistic machine learning. The candidate will join a research team of other FCAI postdocs and professors based at the University of Helsinki and Aalto University (Finland).

Research topics in our group from the current FCAI call are listed below.

  • 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 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 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.

Please see the official call page for further information and instruction on how to apply: https://fcai.fi/we-are-hiring

Please get in touch for informal enquiries (but note that the application will have to go through the official channels above).