PhD/postdoc position

The Department of Computer Science, Faculty of Science, University of Helsinki invites applications for a Doctoral or Postdoctoral Researcher in Resource-Efficient Probabilistic Machine Learning.

The University of Helsinki is an international scientific community of 40,000 students and researchers. It is one of the leading multidisciplinary research universities in Europe and ranks among the top 100 international universities in the world.

The Department of Computer Science, which is part of the Faculty of Science, is a leading Computer Science research and teaching unit in Finland. The research themes of the Department cover machine learning and algorithms, computer networks and distributed systems, software systems and bioinformatics.

The Department of Computer Science, Faculty of Science, University of Helsinki invites applications for a

Doctoral or Postdoctoral Researcher in Resource-Efficient Probabilistic Machine Learning

Position description

The Machine and Human Intelligence research group led by Principal Investigator Luigi Acerbi is looking for a PhD candidate or postdoctoral researcher eager to work on new machine learning methods for smart, robust, sample-efficient probabilistic inference, with applications in scientific modeling — such as, but not limited to, computational and cognitive neuroscience. The candidate will join an established research group with strong links to the Finnish Center for Artificial Intelligence FCAI.

In our group, we develop machine learning approaches for building approximate Bayesian posteriors using only a small number of likelihood evaluations, which can be a game-changer for complex models or when resources are limited. Think of Bayesian optimization, a very effective technique to optimize black-box functions, but scaled up to perform full Bayesian inference. This project will build on the state-of-the-art framework developed in our group, Variational Bayesian Monte Carlo (VBMC), which combines Gaussian process surrogates, active learning, variational inference and Bayesian quadrature (Acerbi, NeurIPS; 2018, 2020).

Example research directions for this project include extending the scalability of VBMC to more complex posteriors and higher dimension (see for example Li, Clarté & Acerbi, arXiv; 2023), for example by leveraging gradients and other available information; exploiting recent advances in Gaussian process inference for superior scalability; strengthening the connections with simulator-based inference; and exploring the theoretical properties of the framework. Concrete applications include fitting complex state-of-the-art models of human decision making in cognitive neuroscience (e.g., van Opheusden et al., Nature, 2023).

The project will include research visits abroad to work with collaborators, such as Michael Osborne's group at the University of Oxford (UK) and Wei Ji Ma's group at New York University (NY, USA). The project will also include collaboration with Antti Honkela's group at the Department of Computer Science of the University of Helsinki, the other team of the consortium "Resource-wise and trustworthy Bayesian machine learning".

The position is full-time, funded for four years (PhD) or 2+2 years (postdoc) and will be filled as soon as possible, with a target starting date in September 2023 (later dates can be negotiated). This project is funded by the Research Council of Finland.


    The work involves research-related activities, including conducting theoretical and applied research, designing and programming machine learning software, computational and data analyses, writing research articles, participating in and presenting research at academic conferences, taking part in international research visits, and teaching-related activities.

    The ideal candidate has a strong background in computational statistics and/or machine learning, and experience with Bayesian methods (e.g., MCMC, variational inference, probabilistic programming, simulator-based inference). The following constitute an advantage, but are not required:

    • Experience in implementing efficient code in Python (e.g., PyTorch).
    • Familiarity with Gaussian processes and/or Bayesian optimization.
    • Experience with computational modeling and statistical model fitting (e.g., in an applied field such as computational neuroscience).
    • Previous research experience and publications (required for a postdoctoral position).

    Excellent written and oral communication skills in English are needed.

    Applicants should have – or be about to obtain – a MSc (for the PhD position) or a PhD (for the postdoctoral position) in machine learning, computer science, data science, applied mathematics and statistics, physics, computational neuroscience, or a related field.

    Applicants for the PhD position who do not currently hold a doctoral student status in the Doctoral Programme in Computer Science at the University of Helsinki are eligible to apply, but in the event of hiring, they are expected to acquire the status during the standard 6-month probationary period.

    Salary and benefits

    The position is full-time and funded for four years (PhD position) or 2+2 years (postdoc). The starting salary is 2400–2800 euros/month (for a doctoral student) and 3600–4200 euros/month (for a postdoc), depending on previous qualifications and experience.

    The University of Helsinki offers comprehensive services to its employees, including occupational health care and health insurance, sports facilities, and opportunities for professional development. The International Staff Services office assists employees from abroad with their transition to work and live in Finland.

    How to apply

    Please submit your application with its attachments to the group leader, Luigi Acerbi (please find email address below), specifying "Application for PhD/postdoc in Resource-Efficient Probabilistic Machine Learning" as the email subject.

    The application should include the following attachments as pdf files (in English):

    1. CV with possible list of publications and previous projects;
    2. A copy of your transcripts (list of courses completed during BSc/MSc and grades);
    3. Cover letter (1-2 pages) with motivation, research interests and match to the project;
    4. Contact details of two referees who could provide a letter of recommendation (can be included at the end of the CV);
    5. An extended sample of your scientific writing (e.g., your Master's or PhD thesis, or a project report; could be a link if available online).

    Applications will be considered until the position is filled.

    Additional information

    For more information on the group and the position, please:

    Please visit the university website for more information on the Doctoral Programme in Computer Science (DoCS).
    For more information on the eligibility and required educational documents to doctoral studies at the University of Helsinki, please visit this page.
    And these are some good reasons to move to Finland.

    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.


    Appendix: Background references and material

    Work related to the VBMC framework:

    • Acerbi (2018). Variational Bayesian Monte Carlo. NeurIPS (link).
    • Acerbi (2020). Variational Bayesian Monte Carlo with Noisy Likelihoods. NeurIPS (link).
    • Li, Clarté & Acerbi (2023). Fast post-process Bayesian inference with Sparse Variational Bayesian Monte Carlo. arXiv preprint (link).
    • Huggins*, Li*, Tobaben*, Aarnos & Acerbi (2023). PyVBMC: Efficient Bayesian inference in Python. arXiv preprint (link).

    Algorithm for simulator-based inference (which pairs well with VBMC):

    • van Opheusden*, Acerbi* & Ma (2020). Unbiased and Efficient Log-Likelihood Estimation with Inverse Binomial Sampling. PLoS Computational Biology (link).

    Example application in cognitive science from our collaborators:

    • van Opheusden, Kuperwajs, Galbiati, Bnaya, Li & Ma (2023). Expertise increases planning depth in human gameplay. Nature (link).