PhD student in data-driven battery modelling Full-time

PhD Student In Data-Driven Battery Modelling

Are you interested in working on real-world data at the interface of battery materials and machine learning, with the support of competent 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 PhD position at the Department of Information Technology, Uppsala University.

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, having 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.

At the Division of Systems and Control, 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. The Division of Systems and Control enjoys a wide network of strong international collaborators all around the world.

Project description: As the world accelerates toward sustainable electrification, advanced energy storage is more critical than ever. Yet, the battery research powering this transition faces a major bottleneck: data integration. While cell manufacturing and advanced characterizations (like X-ray diffraction, imaging, and tomography) generate massive, complex datasets, this information is often limited to statistical analysis. Meanwhile, robust, physics-based battery models remain isolated from these modern data streams.

This PhD project tackles this challenge head-on by bridging data-driven approaches with physics-based modeling. Your mission will be to develop advanced computational methods that unite prior physical knowledge with large-scale manufacturing and experimental datasets. By successfully linking rich data streams, featured in synchrotron characterization and electrode manufacturing processes, to highly predictive and interpretable models, your work will help redefine how battery materials and processes are optimized.

The project is part of COMPEL. COMPEL (COMPetence for the ELectrification of the transport system) is a strategic initiative by the Swedish government, aimed at strengthening research and educational environments within battery technology and electrification. Within COMPEL, the three institutions – Chalmers University of Technology, Lund University, and Uppsala University – in consultation with Swedish industry, deepen their collaboration with the aim of strengthening Sweden's position in battery technology contributing to the electrification of the transport sector, and related areas. The three institutions aim to enhance research and education in the battery sector to ensure strong research in battery technology supporting that electrification of the transport sector is maintained and further developed in Sweden.

At Uppsala University within the Department of Information Technology, COMPEL is a strategic initiative aimed at enhancing research environments in battery technology and electrification, in a manner that complements relevant research at other institutions and builds on our strengths.

Duties: The doctoral student will primarily devote their time to graduate education. Other departmental duties of at most 20%, including teaching and administration, may also be included in the employment.

Requirements: To meet the general entry requirements for doctoral studies, you must:

• hold a Master's (second-cycle) degree in computer science, mathematics or a related field, or
• have completed at least 240 credits in higher education, with at least 60 credits at Master's level including an independent project worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

The University may permit an exemption from the general entry requirements for an individual applicant, if there are special grounds (Chapter 7, § 39 of the Higher Education Ordinance). For special entry requirements, please see the subject's general study plan.

We are looking for candidates with:

• a physics-based modelling and/or machine learning background,
• excellent problem-solving skills,
• high motivation and capacity to persistently work on difficult problems,
• proficiency in programming,
• good communication skills with sufficient proficiency in oral and written English,
• ability to work both independently and collaboratively.

Additional qualifications: Experience or coursework in one or more of the following subjects are particularly valued: dynamical systems, numerical methods, optimization and electrochemistry.

Application: The application must include:

1. a cover letter (at most one page) in English, briefly describing your motivation for applying for this position and the earliest possible employment date. The cover letter should include the heading Suitability for this position, containing a self-assessment on why you would be the right candidate for this position;
2. a CV;
3. degrees and transcript of records with grades (translated to English or Swedish);
4. the Master's thesis (or a draft thereof, and/or some other self-produced technical or scientific text), publications, and other relevant documents;
5. references with contact information (names, emails and telephone number) and up to two letters of recommendation.

About the employment: The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date September 1, 2026 or as agreed. Placement: Uppsala.

For further information about the position, please contact: Yicun Huang, email: yicun.huang@it.uu.se

Please submit your application by May 27, 2026, UFV-PA 2026/1154.