FZ Juelich Master Thesis - Physics-Informed Machine Learning for Dynamic Power System Characterization 2026

At the Institute of Climate and Energy Systems Engineering (ICE-1) they focus on the development of models and algorithms for simulation and optimization of decentralized, integrated energy systems. Such systems are characterized by high shares of renewable energies and increasing sector coupling, which leads to high spatial and temporal variability of energy supply and demand as well as a high degree of interdependence of material and energy flows. The research at the ICE institute aims to provide scalable and faster-than-real-time capable methods and tools that enable the energy-optimal, cost-efficient and safe design and operation of future energy systems.

The transition toward renewable and converter-interfaced energy resources is creating increasingly fast and complex dynamics in modern power systems. In this Master’s thesis, you will develop a physics-informed digital-twinning approach to better characterize these dynamics and estimate relevant electrical and control parameters from transient voltage and current data.

Your tasks will include:

• Developing and analyzing representative dynamic simulation scenarios for electrical power systems
• Extending kernel-based Gaussian Process methods for the estimation of electrical and control parameters
• Integrating physical knowledge, such as governing differential equations and physical constraints, into probabilistic kernel models
• Developing and evaluating a physics-informed digital-twinning methodology based on known system parameters and reference simulations
• Assessing the approach with regard to estimation accuracy, uncertainty quantification, numerical robustness, and computational efficiency
• Analyzing and documenting your results and deriving conclusions for the applicability of the developed approach

• Table of Content
• About Forschungszentrum Julich
• Master Thesis - Physics-Informed Machine Learning for Dynamic Power System Characterization Master's Scholarship
• Aim and Benefits of Master Thesis - Physics-Informed Machine Learning for Dynamic Power System Characterization Master's Scholarship
• Requirements for Master Thesis - Physics-Informed Machine Learning for Dynamic Power System Characterization Master's Scholarship Qualification
• Application Deadline
• How to Apply

Master Thesis - Physics-Informed Machine Learning for Dynamic Power System Characterization Master's Scholarship

Aim and Benefits of Master Thesis - Physics-Informed Machine Learning for Dynamic Power System Characterization Master's Scholarship

• 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, , including project meetings and, where appropriate, conferences.
• Scientific environment: You can expect excellent scientific equipment, modern technologies, and qualified support from experienced colleagues
• Personal responsibility: You organize your tasks independently - from preparation to implementation
• 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
• Work-life balance: We offer flexible working hours 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
• Campus experience: Our research campus in the countryside creates ideal conditions for collegial exchange and sporting activities right on site. Our cafeteria offers a wide range of options—you can enjoy a relaxing lunch break with a lake view
• Perspective: We provide strong support and mentoring to help you prepare for a future career in science or industry. If you have the appropriate qualifications and funding is available, the institute also offers the opportunity to pursue a PhD after completing your master's thesis
• Fair remuneration: FZ Juelich will pay you a reasonable remuneration for your thesis

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

Requirements for Master Thesis - Physics-Informed Machine Learning for Dynamic Power System Characterization Master's Scholarship Qualification

• Excellent university degree (Bachelor) in field of Data Science or a comparable field i.e. Electrical Engineering, Computer Science/ Engineering, Physics or the like
• Strong mathematical background
• Interest in energy systems, power grids and their components
• Excellent knowledge and experience in programming Python
• Excellent knowledge and experience in machine learning
• Excellent ability for cooperative collaboration
• Very good command of written and spoken English with extensive vocabulary is required (at least B2 level according to the CEFR), ideally supported by a certificate confirming the language level. Knowledge of the German language is not mandatory but certainly appreciated

Application Deadline

How to Apply

For more information, kindly visit FZ Juelich webpage.