Research Assistant - PhD Student - AI-based Battery Cell Production: Data-Driven Modeling and Optimization (with PhD Opportunity) (m/f/d)

As the largest university working group in Germany in the field of battery technology and system integration, ISEA is one of the leading international research institutions in this pioneering field at the Faculty of Electrical Engineering and Information Technology at RWTH Aachen. Topics such as digitalization and AI-based modeling and optimisation of battery systems and their production processes are a central focus of the institute's internationally recognized research competence. Our goal is to optimize the development and production of modern battery systems across a wide range of applications. We combine experimental research along the entire value chain with data-driven methods and machine learning. In particular, we explore the relationships between production processes, material properties and cell performance and develop innovative models, simulation approaches and optimization strategies for battery cell production. One focus is on the use of large amounts of data from production and testing to improve the quality, performance and life of battery cells in a targeted manner by means of AI-based methods. With our research we make a significant contribution to the development of efficient, sustainable and intelligent battery systems – for electromobility, stationary energy storage and the energy supply of the future. A special highlight for our employees is the Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL) on campus Melaten. Over 5,000 m2 of state-of-the-art office and laboratory areas provide ideal conditions for interdisciplinary top research. More than 150 scientists work closely connected to the entire value chain – from material research to production and system integration to AI-based energy system analysis. The RWTH is certified as a family-friendly university. RWTH offers a variety of health, counselling and prevention services (e.g. university sports) as part of a university health management. There is a wide range of training and the possibility of obtaining a job ticket for employees and civil servants.

In your activity, you will develop innovative AI-based methods for analyzing, modeling and optimizing production processes of modern lithium-ion battery cells. Use large quantities of data from production and testing to systematically identify and utilize relationships between process parameters and cell quality (e.g. capacity, service life and internal resistance). You design and implement machine learning models for data-driven process analysis and derive concrete optimization strategies for cell production. A special focus is on the development and application of virtual design and simulation tools, with which you predict and evaluate the influence of material and process parameters (e.g. layer thickness or porosity) on electrochemical performance. In addition, you will develop and use explainable AI methods to make the working relationships transparent within the process chain and to identify the most important factors for quality improvement. On this basis, you contribute to the targeted optimization of production processes and help reduce cost-intensive iteration loops in development. You work closely with experimental teams as well as with partners from industry and research, validate your models using real data and bring your results into joint projects, publications and presentations. Overall, you make a central contribution to the data-driven, efficient and sustainable development of battery cell production.

Would you like to work in a top university environment while working closely with industrial partners? Are you interested in the connection of data-driven research and practical engineering research and would like to develop innovative solutions independently? You can also imagine a promotion in the field of modern battery cell production and AI-based methods? You have a very well completed university degree (master or comparable) in a technical or scientific field (e.g. electrical engineering, mechanical engineering, computer science, physics, material science). In addition, ideally bring the following: First knowledge in the field of lithium-ion batteries or great interest in working on this topic Good programming knowledge, especially in Python (e.g. for data analysis and machine learning) Interest in machine learning, data-driven methods and their application in production and manufacturing processes Ideally, first experiences in dealing with larger data sets, statistical analysis or modeling Joy in cooperation in demanding projects with industrial and research partners High motivation, initiative and independent working Teamability and good organizational skills Very good knowledge of German and English in word and writing

What we offer A warm and collegial environment at one of the world's most renowned research institutions in the field of battery technology Divide knowledge growth in a dynamically growing future market (batteries, AI, electromobility, energy storage) Optimum research environment with state-of-the-art infrastructure and close reference to industrial practice Active participation in university teaching and junior funding (e.g. supervision of bachelor's and master's thesis) Possibility to Doctorate at RWTH Aachen in an excellent scientific environment (with Prof. Dr.-Ing. Weihan Li) Our offer The employment is in the employment relationship. The place shall be occupied at the next possible time and shall be limited to 2 years. A possibility to extend to a total of 5 years until the doctorate is planned and desired. The temporary employment is carried out within the framework of the time-limits of the science-time contract law. It is a full-time job. If desired, parttime employment can be made possible. There is a doctoral opportunity. The grouping depends on the TV-L. The place is rated with EG 13 TV-L. The job description is aimed at all sexes. We want to promote the careers of women at RWTH Aachen University and are therefore looking forward to applicants. Women are preferably taken into account in the case of equal suitability, competence and professional performance, provided You are underrepresented in the organisational unit and, if not in the person of a competitor, outweigh the reasons. Applications for suitable people with difficulty are expressly desired. For the purposes of equal treatment, we ask you to waive an application photo. Information on the collection of personal data pursuant to Articles 13 and 14 of the General Data Protection Regulation (GDPR) can be found at https://www.rwth-aachen.de/dsgvo-information-bewerbe