Thesis Work: Offline Reinforcement Learning with Physics-Informed Data-Driven Models

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Västerås
In-Office
Internship
Robotics • Analytics • Energy
The Role
The thesis focuses on model-based offline reinforcement learning, refining physics-based simulators with data, and developing hybrid models using simulations or lab experiments.
Summary Generated by Built In

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 Lead
 

Details

  • 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
Qualifications for the role
  • 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, [email protected], will answer all your questions about the thesis topic and expectations. Recruiting Manager Linus Thrybom, +46 730 80 99 06, 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 www.abb.com to learn more about us and see the impact of our work across the globe.

Top Skills

Python
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The Company
Bengaluru, Karnataka
104,000 Employees

What We Do

ABB is a leading global technology company that energizes the transformation of society and industry to achieve a more productive, sustainable future. By connecting software to its electrification, robotics, automation and motion portfolio, ABB pushes the boundaries of technology to drive performance to new levels. With a history of excellence stretching back more than 130 years, ABB’s success is driven by about 110,000 talented employees in over 100 countries. www.abb.com

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