Doctoral Student in Battery Aging Modeling: The KTH Royal Institute of Technology invites applications for a PhD position focused on developing data-driven models for battery aging in energy systems. This research will contribute to smarter, sustainable building energy systems through advanced optimization techniques in the ALTBESS project.
Designation
Doctoral Student in Data-Driven AI-Based Battery Aging Modeling
| Details | Information |
|---|---|
| Research Area | Energy and Environmental Systems, Energy Technology |
| Location | Stockholm, Sweden |
| Eligibility/Qualification | Master’s degree or 240 ECTS credits, coursework in energy systems, and familiarity with machine learning and optimization methods |
| Salary | Monthly salary according to KTH’s doctoral student salary agreement |
| Position Type | Temporary, Full-time (4 years) |
Description
The position involves developing tailored battery aging models using transfer learning and data-driven techniques, integrating these models into optimization frameworks for aging-aware battery operation, and consolidating outputs into user-friendly software for both researchers and practitioners. You will work closely with academic and industrial collaborators and gain experience in AI, energy system optimization, and battery technologies.
How to Apply
Applicants must submit the following documents through KTH’s recruitment system:
- CV highlighting relevant professional experience and knowledge
- Application letter (max. 2 pages) detailing research interests and future goals
- Copies of diplomas and academic transcripts (translated into English or Swedish if necessary)
- Representative publications or technical reports (summaries with links for longer documents)
Last Date to Apply
Applications must be received by March 12, 2026 at midnight CET/CEST.
