Master Thesis – AI-Accelerated Power Flow Analysis for Synthetic Electrical Distribution Grids - Köln

Forschungszentrum Jülich GmbH

Jülich, Nordrhein-Westfalen, Deutschland

Dein Job

Investigating current challenges and bottlenecks in power flow analysis for large-scale electrical distribution grids
Applying machine learning / AI or surrogate modeling (e.g., neural networks, graph neural networks, physics-informed machine learning) to approximate power flow results
Training models using simulation results generated from conventional power flow solvers
Evaluating AI-based approximators in terms of accuracy, generalization, and computational speed
Integrating models with the existing synthetic grid package
Optionally, contributing to writing a scientific paper on AI-enhanced grid simulations

Required qualifications:

Very good performance in your master’s studies in electrical engineering, computer science, geoinformatics, energy systems, or a related field
Very good knowledge of machine learning and AI algorithms
Solid programming skills in Python and familiarity with machine learning libraries (e.g., scikit-learn, TensorFlow, and PyTorch)
Experience in working with geospatial data (e.g., GeoPandas, Rasterio, and Shapely).
Understanding of electrical power systems, especially power flow basics
Experience or interest in applying machine learning to engineering simulations
Strong analytical skills, ability to communicate and document research results clearly in English (B2)

Desirable qualifications:

Experience with GIS tools and libraries (QGIS, GDAL) and power system simulation tools (e.g., PyPSA, pandapower, etc.)
Knowledge of surrogate modeling, GNNs, or physics-informed machine learning
Experience with academic writing or contributions to scientific papers
High level of independence, motivation, and a structured, reliable work approach
Good team skills and willingness to engage in interdisciplinary collaboration
Please feel free to apply for the position even if you do not have all the required and desirable skills and knowledge.

Flexible Arbeitszeit

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z.T. im Homeoffice

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Jülich, Nordrhein-Westfalen, Deutschland

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Forschungszentrum Jülich GmbH

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