Postdoctoral position in Machine learning for battery timeseries data Full-time

Postdoctoral Position In Machine Learning For Battery Timeseries Data

Are you interested in working with machine learning for batteries, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favourable working conditions? We welcome you to apply for a postdoctoral position at the Department of Information Technology, Uppsala University.

Uppsala University has a long tradition of successful research – among its alumni are 16 Nobel Prize laureates, including, most recently, Svante Pääbo. The University is unique when it comes to combining IT with wider research, from life sciences to the humanities, and this collaboration is currently facilitated by AI4Research and the Centre for Interdisciplinary Mathematics.

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University's third largest department, have around 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website.

At the Division of Systems and Control in the Department of Information Technology, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security.

This position is funded by COMPEL (COMPetitiveness for the ELectrification of the Transport System), a strategic initiative from the Swedish government to ensure Sweden's long-term competitiveness in battery development and the electrification of the transport sector. Research and education within the platform span the entire battery value chain and offer an interdisciplinary network for collaboration. It is within this context that COMPEL is now recruiting several young researchers with different competencies to build a unique interdisciplinary research and education environment. This initiative is based on a strong partnership between academia and industry to drive research and development forward.

Project description: This project aims to develop unsupervised machine learning methods for extracting dynamical models of battery degradation from multimodal timeseries data, with a focus on interpretability. The underlying data comprises high-frequency acoustic emission (AE) measurements, paired with electrochemical measurements, from operating batteries. Such data are known to contain valuable information about complex electro-chemo-mechanical processes—such as particle fracture, interfacial delamination, and gas evolution—but are challenging to analyze. The project aims to move beyond black-box prediction by learning low-dimensional latent representations that capture these underlying physical processes. Examples of possible methodological components include self-supervised temporal representation learning for large volumes of unlabeled AE/electrochemical time-series data, switching state-space models that describe transitions between degradation modes, and neural ODE-based latent dynamics that connect multimodal signals sampled at different rates. Together, these tools will form an integrated framework that both extracts mechanistic insight from complex battery time series and enables interpretable battery health diagnostics and prognostics.

The exact details of the research project will be decided in a dialogue between the postdoctoral researcher and the supervisors Jens Sjölund (machine learning) and Leiting Zhang (battery sensing). The expected outcome is methodology and modelling tools for interpretable and generalizable analysis of large-scale timeseries data, applied to advance the fundamental understanding of battery aging and enable new diagnostic capabilities for real-time battery monitoring.

Duties: A postdoctoral fellow devotes most of their time to research. There is the possibility of teaching up to 20%.

Requirements: PhD degree in machine learning, automatic control, system identification, signal processing, applied mathematics, battery systems, or similar, or a foreign degree equivalent to a PhD degree in said topics. The degree needs to be obtained by the time of the decision of employment. Priority will be given to applicants who have completed their degree no more than three years before the deadline for applications. Due to special circumstances, the degree may have been obtained earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc.

• The applicant must have a strong technical background, specifically, experience in one or more of these subjects are valued: machine learning, automatic control, system identification, optimization, signal processing, filtering and smoothing, probabilistic modelling, dynamical systems, electrochemistry.
• The candidate is expected to have published in the top venues of his or her field, e.g. leading journals in control and applied mathematics and/or top conferences in machine learning.
• Interest in interdisciplinary collaboration.
• Proficiency in programming is required.
• Excellent knowledge of oral and written English is a requirement. You are expected to be able to teach in English.

Additional qualifications: As a person, you are creative, thorough and have a structured approach. When selecting among the applicants we will assess their ability take initiative and move their work forward, to collaborate with others, to have a professional approach and to analyze and work with complex problems. Great emphasis will be placed on personal characteristics and personal suitability.

Application: The application must contain:

• A curriculum vitae (CV),
• A copy of relevant grade documents (translated into Swedish or English),
• A list of publications,
• Up to five selected publications in electronic format
• A research statement describing your past and current research (max 1 page) and a proposal for future activities (max 1 page).
• Contact information for two references.

All applicants should state the earliest possible starting date of employment.

About the employment: The employment is a temporary position of two years according to central collective agreement. Full time position. Starting date as agreed. Placement: Uppsala.

For further information about the position, please contact: Assistant Professor Jens Sjölund, jens.sjolund@it.uu.se, and Assistant Professor Leiting Zhang, leiting.zhang@kemi.uu.se.

Please submit your application by May 11 2026, UFV-PA 2026/1172.