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Postdoktor inom fastfas-batterier: ML-baserad batteriestimering & reglering

Stockholm, Sweden💼 Full-time🗓 2026-07-08 → 2026-07-31

Core

Developing reduced-order models, state estimation methods, and predictive control strategies for solid-state batteries using physics-based modeling combined with machine learning.

Role type

Postdoctoral researcher (control systems & machine learning)

Builds

Physics-informed ML models for battery state estimation and control

Domain

Energy storage / Solid-state batteries / Control theory

Deliverable

production ML models

Required skills

state estimation, dynamic system modeling, control and optimization, machine learning, physics-based modeling, X-ray CT data analysis, quantitative microstructure analysis

Preferred skills

solid-state battery experience, electrochemical system modeling

Technologies

X-ray CT, electrochemical measurement systems

Responsibilities

Develop reduced-order models for coupled electrochemical and mechanical processes; implement state estimation and predictive control strategies; analyze electrochemical measurement data and structural characterization data; reconstruct and segment X-ray CT data; quantify microstructure analysis.

Seniority

Postdoctoral, research-focused IC

Rewrite
## Responsibilities - Develop reduced models for coupled electrochemical and mechanical processes in solid-state batteries. - Create methods for state estimation and predictive control strategies for solid-state batteries based on data from parallel postdoctoral projects within the initiative. - Investigate how operating conditions such as charging protocols, temperature, and mechanical loading affect battery performance, degradation, and lifespan. - Combine physics-based modeling with machine learning for parameter estimation, degradation prediction, and analysis of electrochemical measurement data and structural characterization. - Perform quantitative microstructural analysis, including reconstruction and segmentation of X-ray CT data. - Contribute to the broader initiative on composite solid-state batteries with seven postdoctoral positions across multiple disciplines along the battery value chain, from synthesis, cell manufacturing, characterization, modeling to scaled production. ## Requirements - Completed PhD or equivalent foreign degree in control engineering, electrical engineering, technical physics, applied mathematics, or a related field. - Strong theoretical background in modeling of dynamic systems, state estimation, control, and optimization. - Good knowledge of machine learning. - Proficiency in written and spoken English. - Awareness of diversity and equality issues with a special focus on gender equality. ## Nice to Have - Experience with solid-state batteries and modeling, state estimation, or control of electrochemical systems. - Demonstrated innovative thinking, creativity, and ability to work independently. - Good teamwork and collaboration skills, with an interest in working in a team. ## Benefits - Opportunity to grow and develop in a creative and dynamic work environment with good working conditions and benefits. - Contribution to a leading international technical university driving sustainable development. - Participation in a national battery initiative focused on research and education for the transport sector. - Potential for a secure and stable research position with a focus on innovation and excellence in battery technology.
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