Uppsala University welcomes applications for a PhD position within the Department of Information Technology and the Division of Systems and Control, focusing on Machine Learning and Bayesian models and methods for biomedicine.
Project description
With the recent surge in biomedical data, there exists a need for new mathematical models and machine learning methods that can extract as much information as possible from these data. Advances in computational Bayesian statistics have catalysed the applicability of probabilistic programming frameworks (PPFs) such as Stan, Turing, and Pyro in biomedical research. However, PPFs remain largely underused in biomedical pipelines. This underuse is partly due to a strong tradition in the biomedical community to work with frequentist statistics, and partly due to the fact that life-science data often present with a set of technical challenges that complicate the practical implementation of Bayesian models and methods. This project aims to address these challenges and facilitate the use of Bayesian methods in biomedical research. The selected PhD student will work with Bayesian modelling of life-science systems, computational Bayesian inference with e.g., Hamiltonian Monte Carlo sampling, and Bayesian design of experiments. The project will be supervised by Sara Hamis (Uppsala University) and Eszter Lakatos (Chalmers University of Technology). The PhD student will be located at Uppsala University.
We are looking for candidates with:
- a strong interest in developing Bayesian mathematical models and machine learning methods for biomedical and health research
- good communication skills and sufficient proficiency in oral and written English
- programming experience
- creativity, thoroughness, and a structured approach to problem-solving
- a collaborative mindset and enthusiasm for interdisciplinary work
