Open Positions

We are hiring!

  • Postdoctoral or doctoral position in Probabilistic Machine Learning (FCAI co-supervised). Deadline August 28, 2022.

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 amortized and sample-efficient approaches in probabilistic machine learning. The candidate will join a research team of other FCAI researchers based at the University of Helsinki and Aalto University (Finland).

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

  • AI-powered simulation, optimization and inference
    Recent advances in machine learning have shown how powerful emulators and surrogate models can be trained to drastically reduce the costs of simulation, optimization and Bayesian inference, with many trailblazing applications in the sciences. In this project, the candidate will join an active area of research within several FCAI groups to develop new methods for simulation, optimization and inference that combine state-of-the-art deep learning and surrogate-based kernel approaches – including for example deep sets and transformers; normalizing flows; Gaussian and neural processes – with the goal of achieving maximal sample-efficiency (in terms of number of required model evaluations or simulations) and wall-clock speed at runtime (via amortization). The candidate will apply these methods to challenging problems involving statistical and simulator-based models that push the current state-of-the-art, be it for number of parameters (high-dimensional amortized inference), number of available model evaluations (extreme sample-efficiency) or amount of data. The ideal candidate has expertise in both deep learning and probabilistic methods (e.g., Gaussian processes, Bayesian optimization, normalizing flows).

    Acerbi (2018); NeurIPS:
    Acerbi (2020); NeurIPS:
    Järvenpää & Corander (2021); arXiv:
    Cranmer et al. (2020); PNAS:

    Supervision: Profs. Luigi Acerbi, Jukka Corander, Arno Solin, other professors involved in the topic.
    Keywords: Machine learning, emulators, amortized inference, Bayesian optimization, normalizing flows, simulator-based inference.
    Level: Research fellow, postdoc, PhD student.

  • Synthetic psychologist: optimal experiment design for simulator models in cognitive science
    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. We aim 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, Andrew Howes, Samuel Kaski, Antti Oulasvirta.
    Keywords: Virtual laboratory, cognitive science, simulator models, AI-assisted modeling.
    Level: Research fellow, postdoc.

  • Next-generation likelihood-free inference in ELFI
    ELFI ( 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 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 doctoral students, 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, linked to work in several other FCAI teams. The ideal background requires programming experience with modern deep learning frameworks (e.g., PyTorch) and familiarity with probabilistic inference and simulator-based inference.

    Supervision: Profs. Jukka Corander, Luigi Acerbi.
    Keywords: Machine learning, emulators, likelihood-free inference, simulator-based inference.
    Level: Research fellow, postdoc, PhD student.

Please see the official call page for further information and instruction on how to apply:

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