Thesis Work: Offline Reinforcement Learning with Physics-Informed Data-Driven Models
4 days ago
At ABB, we help industries outrun - leaner and cleaner. Here, progress is an expectation - for you, your team, and the world. As a global market leader, we'll give you what you need to make it happen. It won't always be easy, growing takes grit. But at ABB, you'll never run alone. Run what runs the world.
This Position reports to:
R&D Team LeadDetails
- Period: 5 months (January/February – June/July)
- Number of credits: 30 ECTS
- Number of students for this thesis work: 1
- Location: ABB Research Center (Västerås)
Your role and responsibilities
Advanced control solutions like Reinforcement Learning (RL) often rely on simulators that may not fully capture the real-world process due to noise, disturbances, or modeling limitations.
This thesis explores model-based offline RL, where the model is built using both physics knowledge and data. The work will investigate how we can refine physics-based simulators with data or embed physics knowledge using techniques from the area of Physics-Informed Machine Learning.
Goals:
- Review state-of-the-art model-based Reinforcement Learning approaches
- Investigate techniques in system identification, such as Physics-Informed Neural Networks and their applicability in real-world scenarios
- Develop and validate hybrid models using simulations or lab experiments
- Master's student in Computer Science, Industrial Engineering, or a related field
- Background in Machine Learning, Control and Systems Engineering, or similar disciplines
- Motivation to solve real-world problems using state-of-the-art methods
- Good programming skills (Python)
- Self-driven and solution-oriented
More about us
Supervisor Iga Pawlak, , will answer all your questions about the thesis topic and expectations. Recruiting Manager Linus Thrybom, , will answer your questions regarding hiring.
Positions are filled continuously. Please apply with your CV, academic transcripts, and a cover letter in English. We look forward to receiving your application
Join us. Be part of the team where progress happens, industries transform, and your work shapes the world. Run What Runs the World.
A Future Opportunity
Please note that this position is part of our talent pipeline and not an active job opening at this time. By applying, you express your interest in future career opportunities with ABB.
We value people from different backgrounds. Could this be your story? Apply today or visit to learn more about us and see the impact of our work across the globe.
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