Postdoc position in AI and Machine Learning for Electromobility

Chalmers University of Technology; Göteborgs kommun; On-site; SEK 489,502 - 652,670; Full time

Benefits offered by this job

Access to parental leave

Subsidised day care

Free schools and healthcare

Job summary

Chalmers University of Technology is seeking a post-doc to develop advanced machine learning methods for energy-efficient coordination of electric vehicle fleets during emergencies. The role entails significant research responsibilities and collaboration in a dynamic environment.

The position requires a doctoral degree in a related field and offers access to Sweden's generous benefits, including healthcare and parental leave. This is a full-time role for two years, starting January 1, 2027.

Qualifications

• Strong research experience in AI and machine learning with publications in top tier venues.
• Strong programming skills, preferably in Python and PyTorch.
• Good written and verbal communication skills in English.

Responsibilities

• Develop advanced machine learning models for EV fleet coordination.
• Publish high-quality scientific papers.
• Supervise master’s and PhD students.

Skills

AI and machine learning

Python programming

Communication skills (English)

Education

Doctoral degree in computer science or related field

Tools

PyTorch

Job description

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 the theory and practice of machine learning and AI‑driven decision making. It will be carried out in collaboration with the Automatic Control group (Department of Electrical Engineering) and the Mathematical Optimization group (Department of Mathematical Sciences).

About The Research Project

The project focuses on 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, making efficient large‑scale evacuation planning increasingly important. At the same time, the transport sector is rapidly electrifying. 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 gap presents a significant challenge for designing evacuation strategies that remain effective in future electrified transport systems. The goal is to develop novel AI methods for coordinating fleets of EVs during large‑scale emergency evacuations under energy and infrastructure constraints, leveraging reinforcement learning, deep learning, generative AI, and advanced mathematical optimisation.

Requirements

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

Preferred Experience

• 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.

Responsibilities

• Propose, develop and evaluate advanced machine learning models, including reinforcement learning methods, for 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.

Contract Terms

The position is a temporary full‑time employment for two years, starting on January 1, 2027. Physical presence throughout the entire employment is required. A valid residence permit must be presented by the start date; otherwise, the offer may be withdrawn.

What We Offer

• As a post‑doc at Chalmers, you are an employee and enjoy all employee benefits.
• Dynamic and inspiring working environment in the coastal city of Gothenburg.
• Access to Sweden’s generous parental leave, subsidised day care, free schools, healthcare and other public services.

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.

The application deadline is September 15, 2026.