Postdoc in Solid-State Batteries – ML-Based Battery Estimation & Control, KTH Royal Institute, Sweden

Summary

KTH Royal Institute of Technology is seeking a Postdoctoral Fellow for a two-year project titled “Machine Learning-based Electro-Chemo-Mechanical Estimation and Control of Solid-State Batteries.” Hosted by the Department of Decision and Control Systems within the School of Electrical Engineering and Computer Science (EECS), this position is part of the Swedish governmental battery initiative COMPEL. The research focuses on developing reduced-order electro-chemo-mechanical models, state estimation methods, and predictive control strategies combining physics-based modelling and machine learning to advance health-aware battery management and fast-charging strategies.

Designation

Postdoctoral Fellow (Full-time, Temporary Position)

Position Summary

Research Area

• Solid-State Batteries (SSB)
• Machine Learning & Parameter Estimation
• Systems Modelling & Predictive Control
• Electro-Chemo-Mechanical Modelling
• X-ray CT Image Reconstruction & Quantitative Microstructure Analysis

Location

Stockholm, Sweden

Eligibility / Qualifications

Requirements

• Degree: A doctoral degree or equivalent foreign degree in Control Engineering, Electrical Engineering, Applied Mathematics, Engineering Physics, or a related field (must be completed by the time the employment decision is made).
• Language: Proficiency in written and spoken English.
• Theoretical Knowledge: Strong background in systems modelling, state estimation, optimization, or machine learning.
• Core Values: Awareness of diversity and equal treatment issues, with a particular focus on gender equality.

Preferred Qualifications

• PhD obtained within the last three years prior to the application deadline.
• Experience in solid-state batteries, including modelling, state estimation, or control of electrochemical systems.
• An innovative mindset with the ability to work independently.
• Strong team collaboration and communication skills.

Job Description

• Develop reduced-order electro-chemo-mechanical models for lithium solid-state batteries (SSB).
• Design state estimation methods and predictive control strategies using experimental data from parallel research projects.
• Integrate physics-based modelling with machine learning for parameter estimation, degradation prediction, and structural characterization.
• Perform analysis on electrochemical data and quantitative microstructure data (e.g., X-ray CT image reconstruction and segmentation).
• Study how operating conditions (charging protocols, temperature, mechanical loading) influence battery performance, degradation, and operational lifetime.

How to Apply

Applications must be submitted electronically through the recruitment portal on the KTH Job Announcement Page.

Required Documents:

1. Curriculum Vitae (CV): Including relevant professional experience and knowledge.
2. Diplomas and Transcripts: Copies of university degrees and grades (translated into English or Swedish if original documents are not in a Western European language).
3. Motivation Statement: A maximum 1-page account detailing your research interest, academic background, and future goals.
4. References: Contact details for references or letters of recommendation.

Last Date for Apply

August 15, 2026 (Midnight CET/CEST)

Link