Master Thesis – Machine Learning for Retired Lithium-Ion Cell Sorting
Fraunhofer-Gesellschaft
internship
Posted on:
Location Type: Hybrid
Location: Darmstadt • Germany
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Tech Stack
About the role
- 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
Requirements
- 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
Benefits
- Flexible working conditions with up to 99% remote work
- 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
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningPythonelectrochemistrybattery technologymodel trainingmodel evaluationdata analysisEIS-based analysiscell sortingliterature review
Soft Skills
strong interest in machine learningwillingness to learn