We are looking for a PhD candidate to work on computational statistics and machine learning, with a particular focus on differential equations for machine learning. The PhD project will be concerned with topics such as generative diffusion models and the inference and learning of differential equations in general.

The position is fully funded with an attractive salary.

The project is mainly oriented for theoretical and methodological research. While applied research is possible, it is not the central emphasis.

Qualification

Since the position is more emphasised on fundamental research and machine learning, we prioritie candidates with a strong background in applied mathematics, statistics, and programming skills.

Experience in one or more of the following areas is highly advantageous: stochastic (partial) differential equations, controlled differential equations, generative diffusion models, flow models, optimal transport, stochastic filtering, sequential Monte Carlo, Markov chain Monte Carlo, and Bayesian inference and inverse problems. However, having these prior experience is not mandatory. When evaluating, we focus on candidates' basic understandings, research motivation, and learning potential. For instance, if asked to explain "martingale", do you know what it is (basic understandings), do you feel interested to learn about it if you do not yet know it (research motivation), and will you be able to learn it efficiently (learning potential)? As such, it is encouraged to reflect on the three aspects in your application.

Apply

To apply, please visit https://liu.se/en/work-at-liu/vacancies/28601. The deadline is 10.04.2026. The starting date is flexible, but we expect the candidate to start as soon as possible, and by that time you should have already obtained your Masters degree.

Compile your application files to PDF using LaTeX. Do not submit Word files or similar.

Contact

You will be mainly supervised by me with Fredrik Lindsten as the co-supervisor.