Master Thesis Machine Learning for retired Lithium-Ion Cell Sorting (all genders)

City: Darmstadt Date: Aug 10, 2026 The Fraunhofer-Gesellschaft ( www.fraunhofer.com ) is one of the world's leading organizations for application-oriented research. 74 institutes develop pioneering technologies for our economy and society – more precisely: 30 000 people from technology, science, administration and IT. They know: Anyone who comes to Fraunhofer wants to and can make a difference. For themselves, for us and for the markets of today and tomorrow. Our team at Fraunhofer LBF conducts research into solutions for the design of sustainable materials, structures, and systems, as well as circular economy strategies that meet the highest standards of reliability, efficiency, and resilience. Would you like to be part of this inspiring team? We look forward to receiving your application! Master Thesis Machine Learning for retired Lithium-Ion Cell Sorting (all genders) Darmstadt As electric mobility continues to expand globally, sustainable recycling and second-life utilization of traction batteries are becoming increasingly critical. At Fraunhofer LBF, we are developing an automated disassembly system for electric vehicle batteries. A key component of this process is the rapid and reliable evaluation of individual cell health. The master thesis focuses on the development of a machine learning model for the automatic sorting of used lithium-ion cells based on custom electrochemical impedance spectroscopy (EIS) data. The objective is to build a model that processes EIS measurements and assigns cells to the appropriate sorting category. The sorted cells are subsequently grouped according to their intended secondary use. A dataset for training and testing the model will be provided. In addition, the thesis should investigate how different types of EIS data influence sorting accuracy and model performance. Be part of change Literature review on machine learning methods for battery cell classification and EIS-based analysis Familiarization with the provided EIS dataset Development and training of a machine learning model for cell sorting Evaluation of model performance on test data Investigation of the influence of different EIS data types on sorting accuracy Documentation of the results What you contribute Electrical Engineering / Mechatronics / Computer Science or related fields Strong interest in machine learning Basic knowledge of Python and common machine learning libraries Basic knowledge of electrochemistry and battery technology or willingness to learn What we offer Flexible working conditions with up to 99% remote work Please note: this is an unpaid thesis position An individually tailored task with plenty of creative freedom A highly topical and practically relevant research topic with direct relevance to the circular economy The opportunity to actively participate in an innovative and interdisciplinary project Insight into current developments in battery cell disassembly and diagnostics 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. Our tasks are diverse and adaptable – for applicants with disabilities, we work together to find solutions that best promote their abilities. 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. Ready for a change? Then apply now and make a difference! Once we have received your online application, you will receive an automatic confirmation of receipt. We will then get back to you as soon as possible and let you know what happens next. Do you have questions about the position? Our colleague Savan Dihora is there for you: Phone +49 6151 705-573 Fraunhofer Institute for Structural Durability and System Reliability LBF www.lbf.fraunhofer.de Requisition Number: 84126 Application Deadline: Job Segment: Training, Computer Science, Electrical Engineering, Learning, Education, Technology, Engineering, Research, Human Resources