Master Thesis – Data-Driven System Identification for Dynamic Modeling of rSOC-Systems

A promising technology investigated at the Institute of Energy Technologies - Fundamental Electrochemistry (IET-1) is the reversible Solid Oxide Cell (rSOC), which enables the efficient conversion of electrical energy into hydrogen and vice versa. Coupled with renewable energy sources such as solar and wind power, rSOC systems play an important role in future sustainable energy infrastructures. However, the fluctuating nature of renewable generation subjects these systems to dynamic operating conditions, creating significant challenges for control, efficiency, and long-term durability.

Your Job

In previous projects, a detailed dynamic model of the system was developed based on first-principle (white-box) approaches. While such models provide valuable insight, advanced model-based control methods such as Model Predictive Control (MPC) are often limited by discrepancies between the nominal model and the actual system behavior. The objective of this thesis is to improve model accuracy through data-driven modeling and parameter estimation techniques. A comprehensive experimental dataset is already available, with the possibility of conducting additional experiments if required. Potential approaches include data-driven parameter estimation for white-box models, the development of black-box models using machine learning methods (e.g., neural networks) and their combination (grey-box). The thesis offers the opportunity to work on state-of-the-art research at the interface of control engineering, system identification, machine learning, and sustainable energy systems while contributing to the advancement of hydrogen technologies for the energy transition.

Your responsibilities within the team:

Literature review and familiarization with rSOC, with focus on operation and degradation as well as existing data and experimental setup
Literature review on dynamic modeling (system identification), especially data-driven approaches and comparison of past system identification for rSOC
Development of the digital twin of the rSOC system. Validation on experimental and simulation data.
Analysis of results: Potential comparison of different system models regarding performance, adaptability and real-time applicability. The comparison can include the integration of the model in the current model-based control approach. Opportunity to contribute to a scientific publication

Your Profile

Currently pursuing a master’s degree in automation engineering, process engineering, mechanical engineering or a comparable field of study
Strong interest in control engineering, system dynamics and mathematical modeling
Knowledge in advanced control methods such as nonlinear control preferred
Experience in programming (especially Matlab/Simulink) is preferred
Ability to take initiative and work independently
Excellent collaboration and communication skills, as well as the ability to work as part of a team
Very good command of written and spoken English with extensive vocabulary is required (at least B2 level according to the ), ideally supported by a certificate confirming the language level. Knowledge of the German language is not mandatory but certainly appreciated

Our Benefits for You

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
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
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
Health & well-being: Your health is important to us. You can look forward to a comprehensive company health management programme with a wide range of options, including a beach volleyball court, running groups, yoga classes and much more. In addition, our company medical service and an experienced social counselling team are available to assist you on site
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
Fair remuneration: We will pay you a reasonable remuneration for your thesis

In addition to exciting tasks and a collegial working environment, we offer you much more:

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: and on specific support options:

Place of Employment: Jülich

Start Date: To the next possible date

Application Deadline: 2026-07-27