Postdoc position in AI and Machine Learning for Electromobility

Karlstad University; Göteborgs kommun; On-site; SEK 360,000 - 540,000; Full time

Benefits offered by this job

Dynamic working environment

Employee benefits

Swedish courses for integration

Job summary

Karlstad University is seeking a Postdoc in AI and Machine Learning for Electromobility to develop advanced machine learning methods. The project focuses on energy-aware coordination of electric vehicle fleets during emergencies.

This two-year full-time position begins on January 1, 2027. Ideal candidates will hold a doctoral degree related to AI and possess strong programming skills in Python and PyTorch. Publications in top-tier venues are preferred.

Qualifications

• Strong research experience in AI, machine learning and optimization.
• Publications in top-tier venues related to AI.
• Strong written and verbal communication skills in English.

Responsibilities

• Propose and evaluate advanced machine learning models for EV coordination.
• Publish high-quality scientific papers in relevant venues.
• Contribute to the supervision of master's and PhD students.

Skills

AI

Machine learning

Optimization

Python

PyTorch

Reinforcement learning

Control theory

Education

Doctoral degree in computer science or related field

Job description

Postdoc position in AI and Machine Learning for Electromobility

Our goal is to focus on competence, knowledge and collaboration in order to play an important, demonstrable role in social development.

Do you want to contribute to the future of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy‑aware coordination of electric vehicle fleets to support resilient transport systems during emergency evacuations.

About us

The Department of Computer Science and Engineering, a joint department of Chalmers and the University of Gothenburg, spans the breadth of computing disciplines. Our internationally visible research, strong industry links and diverse environment create a collaborative setting where ideas grow into real impact.

At the division of Data Science and AI, we develop data‑driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and scientific applications.

The project will be conducted at the Machine Learning and Decision Making Lab, which focuses on advancing the theory and practice of machine learning and AI‑driven decision making. It will be conducted in collaboration with the Automatic Control group (from Department of Electrical Engineering) and the Mathematical Optimization group (from Department of Mathematical Sciences).

About the research project

The project focuses on the energy‑efficient coordination of electric vehicle (EV) fleets for resilient transport systems during emergency evacuations. Climate change is increasing the frequency and severity of natural disasters, including floods, wildfires, and extreme weather events, making efficient large‑scale evacuation planning increasingly important. At the same time, the transport sector is undergoing a rapid transition toward electrification. While EVs are central to achieving climate‑neutral mobility, most existing evacuation planning methods still assume conventional fuel vehicles and do not account for EV‑specific challenges, such as limited battery capacity, variable energy consumption, charging infrastructure availability, recharging requirements, and interactions with the electricity grid. This multifaceted gap presents a significant challenge for designing evacuation strategies that remain effective in future electrified transport systems. The project aims to develop novel AI methods for coordinating fleets of EVs during large‑scale emergency evacuations under energy and infrastructure constraints. To address this challenge, the research will leverage reinforcement learning, deep learning, and generative AI, and evaluate against the research front in mathematical optimization strategies, to enable efficient, robust, and adaptive evacuation planning.

Who we are looking for

Mandatory requirements

• A doctoral degree in computer science, artificial intelligence, applied mathematics, physics, electrical engineering, or a closely related field. This eligibility requirement must be met no later than the time the employment decision is made.
• Strong research experience in AI, machine learning and optimization with publications in top‑tier venues.
• Strong programming skills, preferably in Python and PyTorch.
• Strong written and verbal communication skills in English.

Additional experience that strengthens your application

• Publications in A* venues in AI, machine learning, and data science.
• Experience with reinforcement learning, Markov decision processes and/or control theory.
• Experience in fundamental and applied AI/machine learning research.

What you will do

• Propose, develop, and evaluate advanced machine learning models, including reinforcement learning methods, for the energy‑aware coordination of EV fleets.
• Publish high‑quality scientific papers in relevant leading venues.
• Contribute to the supervision of master's and PhD students.

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, starting on January 1, 2027. 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 and inspiring working environment in the coastal city of Gothenburg.
• Chalmers offers Swedish courses to help you settle in if Swedish is not your native language.