Uppsala University is seeking a postdoctoral researcher in unsupervised machine learning for battery timeseries data, supervised by Assistant Professors Jens Sjölund and Leiting Zhang. The position is part of the COMPEL initiative, focusing on the electrification of the transport system and battery development. The research aims to develop interpretable machine learning methods for extracting dynamical models of battery degradation from multimodal timeseries data, including high-frequency acoustic emission and electrochemical measurements. The project emphasizes moving beyond black-box prediction by learning low-dimensional latent representations that capture underlying physical processes in batteries, such as particle fracture and gas evolution.

Methodological components may include self-supervised temporal representation learning, switching state-space models, and neural ODE-based latent dynamics. The research will contribute to mechanistic insight and enable interpretable battery health diagnostics and prognostics. The position is based at the Division of Systems and Control, Department of Information Technology, Uppsala University, which is known for its interdisciplinary research in control theory, machine learning, optimization, and network science, with applications in energy systems, biomedical systems, and more.

Applicants must have a PhD in machine learning, automatic control, system identification, signal processing, applied mathematics, battery systems, or a related field, with a strong technical background in relevant areas. Experience in programming, a record of publication in top venues, and proficiency in English are required. The position is fully funded for two years, with a competitive salary and the possibility of up to 20% teaching. The application deadline is February 2, 2026, and the expected start date is March 1, 2026, or as agreed. Applications should be submitted through Uppsala University's recruitment system and include a CV, grade documents, publication list, selected publications, research statement, proposal for future activities, and references.

This opportunity is ideal for candidates interested in interdisciplinary research at the intersection of machine learning, battery technology, and dynamical systems, offering a collaborative and international environment at one of Sweden's leading universities.