We are seeking a highly motivated candidate to work on the development of statistical physics models for data analysis.

The candidate is expected to work on the use of classical spin models for high order pattern recognition and information coding in binary data. In this context, recent studies have highlighted the existence of linear transformations (called gauge transformations) that map a model into another mathematically equivalent model. The group is interested in understanding how to use these transformations for data analysis. Depending on their background and affinity, the successful candidate may investigate research questions related to: uncovering symmetries in data, understanding information coding in multivariate systems, developing efficient Monte Carlo algorithms for Bayesian inference of high order patterns in data, or using formally simple models for data classification. The project will include applications to real datasets (in particular, possible applications to experimental data from neuroscience experiments).

The group is shared between the Institute for Theoretical Physics (ITFA) in the Institute of Physics (IoP) and the Informatics Institute (IvI). The successful applicant will be embedded in a stimulating multi-disciplinary research environment.

Selection will start July 31 and continue until the position is filled. For further details and the application form, please follow this link.

Contact: Dr. Clélia de Mulatier (