项目介绍

2026年6月17日

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 

The following requirements are mandatory: 

• 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.

The following experience will strengthen 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. Read more about  working at Chalmers  and our benefits  for employees. 
• A dynamic and inspiring working environment in the coastal city of Gothenburg. 
• Read more about Sweden’s generous parental leave, subsidized day care, free schools, healthcare etc at Move To Gothenburg. 

Chalmers is dedicated to improving gender balance and actively works with equality projects, such as the GENIE Initiative for gender equality and excellence. We celebrate diversity and consider equality and inclusion as fundamental aspects of all our activities. 

If Swedish is not your native language, Chalmers offers Swedish courses to help you settle in. 

Application procedure 

The application should be written in English be attached as PDF-files, as below. Maximum size for each file is 40 MB. Please note that the system does not support Zip files. 

CV 

• A comprehensive CV, including a complete list of publications. 
• Details of previous teaching and superv experience. 

Personal letter 

• A brief introduction about yourself. 
• A summary of your previous research fields and key research outcomes. 
• An outline of your future goals and research focus. 

Use the button at the foot of the page to reach the application form.  

A background check may be conducted as part of the application process.

Please note: The applicant is responsible for ensuring that the application is complete. Incomplete applications and applications sent by email will not be considered. Contact details to references will be requested after the interview. 

We welcome your application no later than September 15, 2026.