Research Thesis Opportunity in Energy Systems

vor 3 Wochen


Jülich, Nordrhein-Westfalen, Deutschland Forschungszentrum Jülich Vollzeit
Position Overview:

The growing integration of renewable energy sources presents unique challenges in the development of autonomous energy systems, particularly those not linked to a larger power grid. It is essential to consider extreme scenarios to ensure that energy demand is consistently met. Our team has devised a robust optimization methodology to identify and incorporate these extreme scenarios during the energy system optimization process. This approach, however, results in a multi-tier optimization challenge that can be computationally intensive to resolve.
  • Your responsibilities for this thesis will include the implementation of machine learning surrogate models to substitute one of the optimization tiers, thereby enhancing computational efficiency.
  • You will engage in the development of various machine learning models, such as neural networks and support vector machines, to derive optimal solutions for the optimization challenges.
  • Subsequently, you will evaluate these models across different energy system optimization scenarios, encompassing linear, mixed-integer linear, and non-linear problems.
Candidate Profile:
  • Currently pursuing a master’s degree in engineering, computer science, simulation science, data science, or a related discipline.
  • Proficient programming skills in Python.
  • Preferably familiar with a prominent deep learning framework, such as PyTorch.
  • Fluent in English or German, both spoken and written.
  • Ability to work independently with strong analytical capabilities.
  • Eagerness to learn and adapt to new methodologies.
What We Offer:

We are engaged in addressing contemporary issues that significantly affect our society, providing you with the opportunity to contribute to meaningful change. We support your endeavors with:
  • A clearly defined project topic with specific objectives, facilitating a prompt start.
  • Interdisciplinary collaboration that merges physics, mathematics, computer science, and engineering.
  • Comprehensive guidance from experienced and supportive colleagues.
  • Access to excellent scientific and technical resources.
  • High-quality technical equipment to enable effective remote work.
  • A spacious research campus featuring green areas, promoting networking with peers and opportunities for physical activities alongside work.
In addition to engaging tasks and a collaborative work environment, we provide numerous additional benefits.

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