Postdoctoral Researcher in Reinforcement Learning For Optimization
The Reliability and Risk Engineering Laboratory (RRE) within the Institute of Energy and Process Engineering at ETH Zurich is inviting applications for a post-doctoral researcher to work on the application of reinforcement learning in the domain of energy systems. This challenging project, requiring excellent machine learning and analytical skills, aims to explore the utilization reinforcement learning for complex optimization problems. The project is carried out at the RRE with potential for cooperation with other Labs within the ETH and partners from the Swiss industry. You will work in a highly stimulating environment with state-of-the-art computational infrastructure. This project will provide you with unique opportunities to develop a strong interdisciplinary portfolio in both machine learning and engineered systems and components.
Your main objectives will be to:
Calibrate highly complex physics-based models;
Solve scheduling optimization problems;
Identify optimum operation strategies for engineered systems.
The developed models will be validated against historical and experimental data. Moreover, the results of your work will be published into prominent scientific journals.
We are looking for highly motivated candidates with excellent knowledge in machine learning and in particular reinforcement learning. The successful candidate has good programming skills, preferably Python and Matlab, and experience with tools like TensorFlow and Matlab’s Reinforcement Leaning Toolbox. Furthermore, the candidate has strong analytical skills, is proactive, self-driven with strong problem solving abilities and out-of-the- box thinking. Professional command of English (both written and spoken) is mandatory. You enjoy working in an interactive international environment with doctoral students and other post-docs.
To meet the requirements you should hold a PhD degree in a field related to computer/electrical engineering with excellent knowledge in machine/reinforcement learning or signal processing with experience in deep learning applications. Knowledge in optimization is beneficial.
You will receive an employment full-time contract and a competitive gross salary in accordance with ETH standards. Additionally, support for conference travel and research expenses will be available.