Postdoc in Physics-Informed Machine Learning and System Identification for Battery Systems

Karlstad University; Göteborgs kommun; On-site; SEK 531,149 - 663,937; Full time

Benefits offered by this job

Dynamic working environment

Employee benefits

Job summary

Karlstad University is looking for a postdoc to advance learning and modeling methods in battery systems. This role focuses on developing physics-informed and data-driven approaches to model complex dynamical behavior.

The position offers a dynamic working environment in Gothenburg, with opportunities for research, supervision, and teaching. A doctoral degree in relevant fields, along with strong programming and communication skills, are required for this role.

Qualifications

• Strong written and verbal communication skills in English.
• Strong background in dynamical systems and uncertainty quantification.

Responsibilities

• Pursue research aligned with the project and publish scientific articles.
• Possibility to supervise master’s and co-supervise PhD students.
• Engage in teaching at undergraduate/master’s level.

Skills

System identification

Machine learning

Programming skills (Python/MATLAB)

Scientific machine learning

Optimisation

Education

Doctoral degree in relevant field

Tools

Python

MATLAB

Job description

Postdoc in Physics-Informed Machine Learning and System Identification for Battery Systems

The postdoctoral position focuses on developing advanced learning, inference and modelling methods for complex dynamical systems, with battery systems as a key application domain.

About the research project

Many important processes in battery systems cannot be directly observed from routinely available measurements. The project aims to develop physics-informed and data-driven methods for learning, modelling and predicting complex dynamical behaviour from limited data. The work combines system identification, state estimation, optimisation, scientific machine learning and physical battery modelling, with validation through simulation, laboratory experiments and real-world battery data. The postdoc will work in an interdisciplinary environment at the interface of automatic control, AI and electrochemical energy storage, in collaboration with another postdoc on battery ageing analysis and diagnosis.

Who we are looking for

The following requirements are mandatory:

• A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made
• Strong written and verbal communication skills in English

The following experience will strengthen your application:

• A doctoral degree in automatic control, applied mathematics, electrical engineering, computer science, physics, computational science or a related field.
• Strong background in system identification, dynamical systems, machine learning, scientific machine learning, inverse problems, optimisation, state estimation, uncertainty quantification or related areas.
• Experience in developing and analysing algorithms for complex physical or engineering systems.
• Strong programming skills, for example in Python and/or MATLAB, and a strong publication record.

What you will do

• The major responsibility is to pursue research in line with the project, publish and present high-quality scientific articles, and participate in discussions.
• Possibility to supervise master’s students and co-supervise PhD students.
• Possibility to engage in teaching at undergraduate/master’s level.

The position is meritorious for future roles in academia, industry, or the public sector.

Contract terms

The position is a temporary full-time employment for two years with the possibility of a one-year extension. The position requires physical presence throughout the entire employment. A valid residence permit must be presented by the start date, otherwise the offer may be withdrawn.

What we offer

• As a postdoc at Chalmers, you are an employee and enjoy all employee benefits.
• A dynamic working environment in Gothenburg.

Application procedure

The application should be written in English and attached as PDF files. Maximum size for each file is 40 MB. The system does not support Zip files. Please note that incomplete applications will not be considered. References will be requested after an interview. We welcome your application by the stated deadline.