Postdoc in Machine Learning for Energy Materials - DTU Compute

A two-year postdoc at DTU Compute developing probabilistic modelling and active learning methods for next-generation solar energy materials. Funded by a Villum Synergy grant in the project “Data-driven design of tunable chalcogenide thin films for next-generation indoor and tandem photovoltaics”, the role joins DTU Compute with the Thin-Film Photovoltaics Group at DTU Electro and works closely with an experimental postdoc, using machine learning to understand how materials form and to guide the next synthesis experiment.

The position includes contributions to teaching and supervision of PhD, MSc and BSc students. Expected start: 1 January 2027 or by agreement.

A PhD (or equivalent) in machine learning or a related field, with an interest in methods for scientific discovery and energy materials.