At the Institute of Energy and Climate Research – Juelich Systems Analysis (ICE-2), the ICE-2 team "Integrated Infrastructure–Distribution Infrastructure" develops synthetic, geo-referenced distribution grids for electricity, gas, and hydrogen. These efforts support the planning of robust, climate-neutral infrastructure systems by analyzing load profiles, technology placement, and sector integration. By joining us, you`ll contribute to Germany`s energy transition and digital transformation through cutting-edge research.
Your Job:
- Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids
- Apply machine learning/AI or surrogate modeling (e.g., neural networks, graph neural networks, physics-informed ML) to approximate PF results
- Train models using simulation results generated from conventional power flow solvers
- Evaluate AI-based approximators in terms of accuracy, generalization, and computational speed
- Integrate models with the existing synthetic grid package
- Optionally contribute to writing a scientific paper on AI-enhanced grid simulations
Table of Content
Summary
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Benefits
- MEANINGFUL TASKS: Your thesis deals with a future-oriented, socially relevant topic with direct practical relevance in an international environment
- PRACTICAL RELEVANCE: With us, you will gain valuable practical experience alongside your studies and actively participate in interdisciplinary projects
- SCIENTIFIC ENVIRONMENT: You can expect excellent scientific equipment, modern technologies, and qualified support from experienced colleagues
- ONBOARDING & TEAMWORK: You can look forward to working in a dedicated, international, and collegial team. It is important to us that you quickly settle into the team and are given structured training for your tasks. We also support you from the very beginning and make your start easier with our Welcome Days and Welcome Guide: https://go.fzj.de/welcome
- WORK-LIFE BALANCE: We offer flexible working hours, possible 100% home office, to help you balance your professional and personal life. You also have the option of flexible working (in terms of location), which is generally possible after consultation and in line with upcoming tasks and (on-site) appointments
- FLEXIBILITY: Flexible working hours make it easier for you to balance work and study
- FAIR REMUNERATION: We will pay you a reasonable remuneration for your thesis
- FIXED-TERM: The position is initially for a fixed term of 6 months
Requirements
- Very good performance in your Master’s studies in Electrical Engineering, Computer Science, Geoinformatics, Energy Systems, or 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, PyTorch)
- Experience working with geospatial data (e.g., geopandas, rasterio, 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), power system simulation tools (e.g., PyPSA, pandapower, etc)
- Knowledge of surrogate modeling, GNNs or Physics-Informed ML
- 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
Application Deadline
Not SpecifiedHow To Apply
Are you qualified and interested in this opportunity? Kindly go to
Forschungszentrum Jülich on www.fz-juelich.de to apply
For more information, kindly visit FZ Juelich webpage.
