Postdoc in Physics-Informed Machine Learning and System Identification for Battery Systems Chalmers University of Technology in Sweden

Chalmers University of Technology is offering a postdoctoral position in Physics-Informed Machine Learning and System Identification for Battery Systems within the Division of Systems and Control, Department of Electrical Engineering in Gothenburg, Sweden.

The project sits at the intersection of automatic control, machine learning, scientific machine learning, optimisation, state estimation, and physical battery modelling. The research aims to develop new physics-informed and data-driven methods for learning, modelling, and predicting complex dynamical behaviour from limited measurements. A key application domain is battery systems, where many important internal processes cannot be directly observed using routinely available data. The work will involve algorithm development, computational methods, theoretical analysis, and validation using simulation, laboratory experiments, and real-world battery data.

The postdoc will work in an interdisciplinary environment and collaborate closely with another postdoctoral researcher focused on battery ageing analysis and diagnosis. The project also connects to academic and industrial collaboration opportunities, including Uppsala University and industrial partners such as Volvo Trucks, ABB, and Husqvarna. The position is mainly funded by the Swedish Research Council, and there may also be an opportunity to be hosted by the group to apply for a Marie Skłodowska-Curie Postdoctoral Fellowship in 2026.

Applicants must hold a doctoral degree or an equivalent foreign degree by the time the employment decision is made. Strong English communication skills are required. Experience in automatic control, applied mathematics, electrical engineering, computer science, physics, computational science, or a related field is valued, especially with backgrounds in system identification, dynamical systems, machine learning, inverse problems, optimisation, state estimation, uncertainty quantification, or similar areas. Strong programming skills in Python and/or MATLAB and a strong publication record are advantageous.

The appointment is a temporary full-time postdoctoral employment for two years with the possibility of a one-year extension. The role requires physical presence throughout the employment. The application must be submitted in English via the online system as PDF files and should include a CV, publication list, relevant coursework, teaching experience if applicable, and a personal letter describing motivation, fit, research experience, technical skills, achievements, and initial scientific ideas. Applications are due by 2026-07-31, and candidates are encouraged to apply early because review and interviews may begin before the deadline.