Spontaneous applications of strong candidates with statistical mechanics, soft matter and/or biophysics background are always encouraged, for internships, PhD and postdoc positions. Candidates with a more mathematical background (statistics, machine learning, stochastic processes...) are also encouraged to apply. Informal inquiries are welcome; make sure you indicate the specifics of your interest and the relevance of your experience to the group's activity in your application. We largely disregard generic applications.
We are looking for a PhD student interested in the theory and practice of inference for stochastic dynamics, at the interface of statistical physics, machine learning and quantitative biology. Possible directions include nonlinear parametric and nonparametric inference, combinations between neural-network and interpretable model discovery, and the exploitation and expansion of our data-augmentation framework for systems with hidden variables. A second axis focuses on active biological systems (e.g. cell migration, embryonic tissues), where these methods will be applied and further developed. Candidates should have a strong background in physics, applied mathematics or a related field, and a serious interest in both theory and computation.