Postdoc in solid-state batteries – ML-Based Battery Estimation & Control

Kungliga Tekniska högskolan; Stockholms kommun; On-site; SEK 320,000 - 420,000; Full time

Job summary

KTH Royal Institute of Technology invites applications for a two-year postdoctoral researcher position in solid-state batteries under the COMPEL program. The project blends physics-based modelling with machine learning for state estimation and predictive control, aiming to understand how charging conditions, temperature and mechanical load influence degradation and lifetime.

The role involves developing reduced-order models, integrating ML with physics, and collaborating across related COMPEL

Qualifications

• Proficiency in written and spoken English.
• Strong theoretical background in systems modelling, estimation, optimization or machine learning.
• Awareness of diversity and equal treatment issues with focus on gender equality.

Responsibilities

• Develop reduced-order electro-chemo-mechanical models and state estimation methods for solid-state batteries.
• Integrate physics-based modelling with machine learning for parameter estimation and degradation prediction.
• Analyze electrochemical and structural characterization data, including X-ray CT image reconstruction and analysis.
• Explore how charging protocols, temperature and mechanical loading affect performance and lifetime.
• Collaborate with parallel COMPEL projects and contribute to health-aware battery management.

Skills

English proficiency

Systems modelling

Estimation

Optimization

Machine learning

Education

Doctoral degree in Control Engineering or related field

Job description

Postdoc in solid-state batteries – ML-Based Battery Estimation & Control

KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science

The lithium solid-state batteries (SSB) initiative aims to replace flammable organic electrolytes with safer, more compact solid electrolytes. Within the framework of the Swedish governmental battery initiative COMPEL, KTH collaborates on a coordinated SSB program with several postdoctoral positions spanning synthesis, cell assembly, characterization, modeling and scaled-up manufacturing. The 2-year postdoctoral project Machine Learning-based Electro-Chemo-Mechanical Estimation and Control of Solid-State Batteries will be hosted by the Department of Decision and Control Systems (EECS). The project focuses on developing reduced-order electro-chemo-mechanical models, state estimation methods and predictive control strategies for solid-state batteries using data from parallel COMPEL projects. The research aims to understand how operating conditions, including charging protocols, temperature and mechanical loading, influence performance, degradation and lifetime. The work combines physics-based modelling with machine learning for parameter estimation, degradation prediction, and analysis of electrochemical and structural characterization data, including X-ray CT image reconstruction, segmentation and quantitative microstructure analysis, with the long-term goal of enabling health-aware battery management and optimized charging strategies.

Responsibilities

• Develop reduced-order electro-chemo-mechanical models and state estimation methods for solid-state batteries.
• Integrate physics-based modelling with machine learning for parameter estimation and degradation prediction.
• Analyze electrochemical and structural characterization data, including X-ray CT image reconstruction, segmentation and microstructure analysis.
• Investigate how operating conditions (charging protocols, temperature, mechanical loading) affect performance, degradation and lifetime.
• Collaborate with parallel postdoctoral projects within the COMPEL initiative and contribute to health-aware battery management and optimized charging strategies.

Qualifications

Requirements

• A doctoral degree or an equivalent foreign degree in Control Engineering, Electrical Engineering, Applied Mathematics, Engineering Physics, or a related field. This eligibility requirement must be met no later than the time the employment decision is made.
• Proficiency in written and spoken English, as this will be required in day-to-day work.
• Strong theoretical background in systems modelling, estimation, optimization or machine learning.
• Awareness of diversity and equal treatment issues with a particular focus on gender equality.

Preferred qualifications

• A doctoral degree or an equivalent foreign degree 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, is considered a merit.
• An innovative mindset and ability to work independently.
• Good collaborative skills and the ability to work in a team.

Great emphasis will be placed on personal skills.

Join us at KTH

KTH shapes the future through education, research and innovation. As a leading international technical university, we play an active role in advancing the transition towards a sustainable society. At KTH, you have the opportunity to grow and develop in a creative and dynamic environment, with good working conditions and attractive benefits. Equality, diversity and equal opportunities are essential to quality and form an integral part of KTH’s core values as a university and public authority.

Log into KTH's recruitment system to apply for this position. You are responsible for ensuring that your application is complete according to the instructions in the ad.

Application materials

• CV including relevant professional experience and knowledge.
• Copies of diplomas and grades from university studies, with translations into English or Swedish if originals are not in a West-European language.
• Brief account of research motivation, interests and how they relate to previous studies and future goals (max one page).
• Contact information for references or recommendation letters.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).

About the employment

The position offered is for, at the most, two years.

It may be the case that a position at KTH is classified as a security-sensitive role in accordance with the Protective Security Act (2018:585). If this applies to the specific position, a security clearance will be conducted for the applicant in accordance with the same law with the applicant's consent. In such cases, a prerequisite for employment is that the applicant is approved following the security clearance.

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Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.