IEK-10 focusses on the optimal design and operation of integrated, decentralized energy systems with a high share of renewable energy. Computer simulation and numerical optimization are our essential tools to arrive at efficient, reliable, and cost-effective solutions. We contribute both to the development of mathematical models and to the development of improved simulation methods and optimization algorithms. Our methods and software-tools are validated against operating data of real systems. Furthermore, we conduct comprehensive case studies to test and further improve the scalability and the performance of our models and algorithms.
Context and Assignment
At IEK-10, we are looking for an ambitious student for a master thesis. The goal is to apply new methods from the field of data science and machine learning to problems in energy systems engineering. The recent development in machine learning has allowed for ‘model-free’ learning of data distributions. Combined with numerical optimization and optimization under uncertainty we want to leverage the power of machine learning to gain additional knowledge about relevant energy systems data. We will use new methods to improve the results of optimal energy-system operation problems.
Your profile
- Very good student in the field of Energy- or Process Systems Technology, Computational Engineering Science, Data-/Simulation Science, or similar
- Programming experience in Python or Matlab
- Interest in data science, numerical optimization, and energy systems
Our offer
- A highly motivated group in one of Europe’s biggest research centers
- Excellent scientific infrastructure
- Working environment with clear focus on numerical optimization and
- Supervision by experts in relevant fields
Contact
Eike Cramer
Forschungszentrum Jülich
Institute of Energy and Climate Research
Energy Systems Engineering (IEK-10)
E-Mail: e.cramer@fz-juelich.de