Open Postdoc and PhD Positions at Helsinki Probabilistic Machine Learning Lab

The Helsinki Probabilistic Machine Learning Lab is looking for Postdoctoral Researchers and PhD Candidates to contribute to research in probabilistic machine learning theory and its applications.

Open Positions:

  • Winter 2025 PhD and Postdoc Positions in AI and Machine Learning (FCAI)
    Supervised by Helsinki Probabilistic Machine Learning PIs through the Finnish Center for Artificial Intelligence (FCAI). (Deadline: February 2, 2025) FCAI and ELLIS Unit Helsinki invite applications for research positions in machine learning. You will join one of the top AI research centers in the Nordics and Europe, with access to an excellent network of scientists and a broad range of opportunities to collaborate with companies. [More info].
     
  • Postdoctoral Position in Probabilistic Machine Learning and Amortized Inference
    Hosted by the Machine and Human Intelligence research group. This postdoctoral position is open to candidates eager to work on amortized and sample-efficient (surrogate-based) approaches in probabilistic and simulator-based inference. The successful candidate will join our group at the University of Helsinki, with active collaborations within the Finnish Center for Artificial Intelligence (FCAI) and internationally (New York, Geneva, Paris). This position will be filled as soon as possible, with the starting date to be negotiated. [More info].
     
  • Postdoctoral Researchers in Probabilistic Modeling and Machine Learning for Science
    Available in the Multi-Source Probabilistic Inference research group. (Deadline: February 2, 2025) We are seeking 2-3 postdoctoral researchers to join the group, focusing on the following topics:
    1. Probabilistic methods, with specific interest in efficient approximate inference (MCMC, Laplace, variational approximation) and flexible models (GPs, flows, diffusion models, etc.).
    2. Collaborative AI and human modeling, with specific interest in eliciting and utilizing tacit human knowledge.
    3. Machine learning for science, with specific interest in virtual laboratories as domain-agnostic platforms for assisting scientific research, and machine learning for ultrasonics and food science applications. [More info].

For details, please click the "More info" links or contact the team.