Open positions

This page provides information on how to join the group.

Current open calls

Fall 2025: You can apply for doctoral studies within the research group through the , with deadline on October 31st, 2025

We consider all applicants interested in the general themes listed below. When applying, please describe your own interests and explain how you feel the relate to ours. To ensure we notice your application, it is good to explicitly list the group PI (Arto Klami) as potential supervisor. 

We are flexible with the supervision arrangements. The group PI counts as the required "ELLIS Fellow / Scholar / Unit Member", and hence is eligible to supervise ELLIS PhD students with any other ELLIS Member within Europe. You can either already have another supervisor in mind, or we can find a suitable additional supervisor together after the review process.

 

General interest in probabilistic modelling and machine learning for science

We will always consider exceptional students interested in doctoral studies and strong candidates for post-doctoral researcher positions. Please contact the group leader directly if interested. Some general topics of interest the group is working on right are listed below, and you are advised to relate your interests to these topics. Check also for possible open positions in artificial intelligence research in several research groups in the Helsinki area.

1. Probabilistic methods, with specific interest on efficient approximate inference (MCMC, Laplace, variational approximation) and flexible models (GPs, flows, diffusion models etc). Examples of our recent activities and work in this area: 

  • Research Council of Finland projects Efficient Riemannian Inference (2022-24) and Computationally Efficient Inference on Riemann Embedding Manifolds (2023-25)
  • Yu et al. , AISTATS, 2024
  • Luu et al. , NeurIPS, 2024
  • Williams et al. , PGM, 2024
  • Kusmierczyk et al. , NeurIPS, 2019

2. Collaborative AI and human modelling, with specific interest in eliciting and using tacit human knowledge. We develop theory and methods for eliciting human beliefs and knowledge as probabilistic quantities, to be used e.g. as prior information in scientific statistical models, or for explaining human behaviour. Examples of our recent activities and work:

  • Research Council of Finland project Flexible priors for flexible models (2024-2028)
  • Mikkola et al. , NeurIPS, 2024
  • Mikkola et al. , Bayesian Analysis, 2024
  • de Souza da Silva et al. , JMLR, 2023
  • Hartmann et al. , UAI, 2020

3. Machine learning for science, with specific interest in virtual laboratory as domain-agnostic platform for assisting scientific research. We work e.g. on Bayesian optimization, physics-informed machine learning, neural operators, and on the general question of how to best use machine learning solutions within scientific discovery. The work can be done in context of several application areas, ranging from ultrasound physics to food science. For incance, we work on AI-assisted design of novel food production processes via use of plant and fermentation-based proteins, as part of the . Examples of our recent activities and work:

  • Research Council of Finland projects Food Innovation and Diversification to Advance the Bioeconomy (2025-27) and Sustainable Industrial Ultrasonic Cleaning (2023-25), Business Finland project Virtual Laboratories for pharmaceutical R&D (2023-25)
  • Klami et al. , Data-Centric Engineering, 2024
  • Gharib et al. , Mechanical Systems and Signal Processing, 2025
  • Mikkola et al. , AISTATS, 2023