Postdoc in Data-driven Modelling and Optimisation for Fast Charging of Lithium-Ion Batteries Chalmers University of Technology in Sweden

This postdoctoral position at Chalmers University of Technology offers a unique opportunity to advance research in data-driven modelling and optimisation for fast charging of lithium-ion batteries. Hosted by the Division of Systems and Control within the Department of Electrical Engineering, the project addresses critical challenges in battery ageing and safety, focusing on lithium plating and its impact during rapid charging cycles. The research aims to develop safer, faster, and longer-lasting charging strategies for Li-ion cells through two complementary approaches: testing commercial cells under various stress factors to identify optimal fast-charging parameters using data-driven optimization, and designing optimal control strategies on laboratory three-electrode cells with anode potential feedback. Transfer learning and robust control methods will be applied to generalize findings to commercial battery cells.

The project is funded by the Swedish Research Council and the Swedish Innovation Agency (Batteries Sweden), ensuring full-time employment for two years with the possibility of a one-year extension. The postdoc will collaborate with leading partners including Uppsala University, Volvo Trucks, ABB, and Husqvarna, and may be eligible to apply for the Marie Skłodowska-Curie Postdoctoral Fellowship in 2026. Chalmers provides a dynamic and inspiring work environment in Gothenburg, with employee benefits such as parental leave, subsidized day care, free schools, and healthcare. The university is committed to gender equality and offers Swedish language courses for international staff.

Applicants must hold a doctoral degree or equivalent by the time of employment decision and demonstrate strong written and verbal English skills. Preferred backgrounds include automatic control, electrical engineering, computational materials science, or related fields, with research experience in battery testing, machine learning, data-driven optimization, and robust control. The position requires physical presence in Gothenburg and a valid residence permit at the start date.

To apply, submit your application online by April 24, 2026, including a comprehensive CV, publication list, teaching experience summary, and a personal letter outlining your research background and future goals. Incomplete applications and those sent by email will not be considered. References will be requested after the interview. For further information, contact Professors Changfu Zou and Torsten Wik at Chalmers University of Technology.