Current open calls for positions are listed below. Exceptional students interested in doctoral studies and strong candidates for post-doctoral researcher positions can also contact the group leader outside the specific calls.
Summer internship (deadline February 3rd, 2020)
We are looking for a summer intern to work on probabilistic machine learning and Bayesian statistics. We are primarily interested in candidates with strong interest in research in the field (eventually aiming for doctoral studies), and can fine-tune the specific project depending the skills and interests of the candidate. Possible topics include (a) Prior elicitation for incorporating domain knowledge into Bayesian models, (b) Human-like behavioral explanations for e-commerce and mobile gaming activity, (c) Efficient approximate inference algorithms for Bayesian modeling, (d) Gaussian process models.
The call is targeted for students already living in Finland and working towards their BSc or MSc at University of Helsinki. The positions is suitable for students of computer science, applied mathematics, statistics and physics, assuming sufficient background knowledge of machine learning and statistical modeling.
Post-doctoral researcher (deadline January 27th, 2020)
HIIT has joint call for postdocs and research fellows with multiple topics. General applications for our group can be sent for topics 1 (Agile Probabilistic AI) and 8 (Easy and privacy-preserving modeling tools), with open topic proposals within the broad field of Bayesian machine learning.
For a more specific project, see topic 36 Machine Insight for Behavioral Analytics:
We seek two postdoctoral scholars for a project at the intersection of machine learning and behavioral analytics. We aim at the development of inference and visualization methods that can offer human-like behavioral explanations to business data. We pursue open source methods that can be widely deployed in analytics. The project team combines our existing experience at FCAI in probabilistic machine learning and statistical modeling with established modeling in behavioral and decision science. The behavioral models are needed as they are able to explain human behavior based on observed data, whereas machine learning is needed to provide powerful methods for solving complex inference tasks required for grounding the behavioral models on observed data. The research is lead by professors Arto Klami and Antti Oulasvirta, but the project is done in close collaboration with other FCAI key professors