Graduate Thesis in Machine Learning for Energy Systems
vor 3 Wochen
The growing integration of variable renewable energy sources presents new challenges in the design of autonomous energy systems, specifically 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 developed a robust optimization methodology to identify and incorporate these extreme scenarios during the energy system optimization process. However, this results in a complex multi-level optimization challenge that can be computationally intensive to resolve.
- Your responsibilities for this thesis will include implementing machine learning surrogate models to streamline one of the optimization levels, thereby enhancing computational efficiency.
- You will explore various machine learning techniques, such as neural networks and support vector machines, to ascertain the optimal solutions for specific optimization challenges.
- Subsequently, you will evaluate these models across different examples of energy system optimization problems, which may include linear, mixed-integer linear, and non-linear scenarios.
- Currently enrolled in a master’s program in engineering, computer science, simulation science, data science, or a related discipline.
- Proficient programming skills in Python.
- Experience with a leading deep learning framework, such as PyTorch, is preferred.
- Fluency in English or German, both spoken and written.
- Ability to work independently and possess strong analytical skills.
- Openness to learning new methodologies.
We are engaged in addressing pressing societal issues and provide you with the opportunity to contribute to meaningful change. Our support includes:
- A clearly defined project with specific objectives, facilitating a quick start.
- Interdisciplinary collaboration that merges physics, mathematics, computer science, and engineering.
- Comprehensive guidance from experienced colleagues.
- Access to excellent scientific and technical resources.
- Top-notch technical equipment to enable effective remote work.
- A spacious research campus with green areas, fostering networking opportunities and promoting physical activities alongside professional commitments.
This position is designed for a duration of up to 12 months.
We encourage applications from individuals with diverse backgrounds, including variations in age, gender, disability, sexual orientation/identity, and social, ethnic, and religious origins. We value a diverse and inclusive workplace that provides equal opportunities for all to reach their full potential.
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