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

Helmholtz Association of German Research Centres; Deutschland; Hybrid; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

A leading research organization in Germany is seeking a student for a diploma & master thesis role focusing on power flow analysis and machine learning. You'll investigate key challenges, train models with simulation data, and work in a collaborative scientific environment. Ideal candidates have strong backgrounds in Electrical Engineering, Computer Science, or related fields, with proficiency in machine learning tools. Gain valuable experience while working on impactful, socially relevant research.

Leistungen

Flexible working hours

Home office options

Supportive onboarding

Fair remuneration

Qualifikationen

• Very good performance in your Master’s studies in Electrical Engineering, Computer Science, Geoinformatics, Energy Systems, or related field.
• Understanding of electrical power systems, especially power flow basics.
• Experience or interest in applying machine learning to engineering simulations.

Aufgaben

• Investigate current challenges in power flow analysis for electrical distribution grids.
• Train models using simulation results generated from conventional power flow solvers.
• Integrate models with the existing synthetic grid package.

Kenntnisse

Machine Learning and AI algorithms

Python programming

Geospatial data handling

Analytical skills

Communication in English (B2)

Ausbildung

Master's degree in relevant field

Tools

scikit-learn

TensorFlow

PyTorch

QGIS

GDAL

Jobbeschreibung

Area of research

Diploma & Master Thesis

Job description

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

Your Profile

Required Qualifications:

• 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

Please feel free to apply for the position even if you do not have all the required and desirable skills and knowledge.

Our Offer

We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with:

• 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: 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

In addition to exciting tasks and a collegial working environment, we offer you much more: go.fzj.de/benefits

Further information on the project is available at: www.fz-juelich.de/de/ice/ice-2/ice2-forschung/integrierte-infrastruktur/verteilnetze

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.

The following links provide further information on diversity and equal opportunities: go.fzj.de/equality and on specific support options: go.fzj.de/womens-job-journey

This research center is part of the Helmholtz Association of German Research Centers. With more than 42,000 employees and an annual budget of over € 5 billion, the Helmholtz Association is Germany's largest scientific organisation.