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Phd positions in Probabilistic Machine Learning at #AaltoPML group Finland

There are PhD positions in our Probabilistic Machine Learning group at Aalto, Finland, and altogether 15 positions in Helsinki ICT network. Apply here

The most interesting topic in the call is supervised by Prof. Samuel Kaski at AaltoPML (and you may collaborate with me too :)

We are looking for PhD candidates interested in probabilistic modeling and machine learning, both theory and applications. Main keywords include Bayesian inference and multiple data sources. Strong application areas with excellent collaboration opportunities are: personalized medicine, bioinformatics, user interaction, brain signal analysis, information visualization and intelligent information access. The group has several excellent postdocs who participate in supervision. We belong to the Finnish Center of Excellence in Computational Inference Research COIN.

Although this description doesn’t mention it, the research may also be related to Stan.

And before Andrew comments, I just say that right now in the winter, south Finland is warmer than New York or Iceland!


  1. Walter Reade says:

    Had I (played and) won the PowerBall, I would be on a plane to Finland. Cool research in a beautiful country! (Since I live in Wisconsin, the cold is of no concern.)

  2. Keith O'Rourke says:

    Aki – “in the winter, south Finland is warmer than New York”

    Trust you have a comparable daily temperature charts over the past few years that you are willing to share?

    Or, have you decided, like your colleague to change your academic game plan and claim its confidential

  3. Aki Vehtari says:

    Another Stan related topic in the call is
    Scalable Probabilistic Analytics (Prof. Petri Myllymäki)

    We are looking for a PhD candidate interested in probabilistic modelling and Bayesian inference, to work on developing computationally efficient probabilistic modelling tools. The position is funded by a Tekes project that aims to speed up the process of developing probabilistic tools for analytics demands, by combining easy-to-use probabilistic programming languages with efficient distributed inference backend. The project combines fundamental basic research with industry collaboration and opportunity to contribute to open-source software development. An ideal candidate has strong knowledge in machine learning and probabilistic modelling, with sufficient programming skills. Contact person: Academy Research Fellow Arto Klami.

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