Research Assistant for Determining the Remaining Useful Lifetime of EV Batteries

The Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG conducts research at seven locations in the fields of integrated energy infrastructures, geothermal energy and sector coupling for a successful energy transition. Our institute conducts applied research, develops innovative technologies for public and industrial clients and translates these into marketable products and processes.

Our competence center for “Monitoring and Artificial Intelligence” researches and develops innovative AI and monitoring methods across numerous fields of energy infrastructures and geothermal energy systems. We develop and use AI-based applications with a focus on the development of analytical methods for the analysis of large data sets, feature extraction, automatic pattern recognition, anomaly detection and predictive analytics.

The aim of the BASE project is to develop a trustworthy and interoperable framework for the Digital Battery Passport (DBP) to ensure traceability and sustainability in the battery value chain. The DBP will provide up-to-date and accurate data on battery performance indicators, remaining useful life, disassembly, material composition and safety.

More information on BASE: Nur die transparente Batterie wird grün

We are looking for a research assistant to support us at our location in Bochum.

What you will do

• You will conduct literature research on modern approaches for predicting the Remaining Useful Lifetime (RUL) of batteries.
• You will implement and experiment with different RUL prediction models using data-driven and physical methods.
• The evaluation and comparison of these models are also on your agenda.
• In addition, you will analyze, evaluate and compare the models to identify strengths, limitations and areas for improvement.
• Thorough documentation of the methods, results and findings is also required.

What you bring to the table

• Matriculated Master's student majoring in electrical engineering, data science, computer science, physics or a related field.
• Profound knowledge of at least one programming language, preferably Python.
• Previous experience in machine learning and deep learning.
• Practical experience with frameworks such as Keras or PyTorch
• Good written and oral communication skills in German and English.

What you can expect

• You will work as part of a diverse and interdisciplinary consortium on a large EU-funded research project.
• You will have the opportunity to combine your work with a Master's thesis, supported by experienced scientists.
• In addition, you will gain insights into applied AI methods for real-world applications and challenges.
• A practice-oriented and future-oriented work activity
• Our supervisors make you strong so that you are successful
• Flexible working hours that fit in with your studies
• Well-equipped technical infrastructure at your workplace

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Remuneration according to the general works agreement for employing assistant staff.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future. 

Interested? Apply online now. We look forward to getting to know you!

We will be happy to answer any questions you may have about this position:

Dr. Shahin Jamali and Dr. Noah Biederbeck

via E-Mail

Questions about the application process will be answered by:

Herr Philipp Steinborn

Tel: +49 355 35540 172

via E-Mail

Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG 

www.ieg.fraunhofer.de 

Requisition Number: 79763                Application Deadline: 06/15/2025