Machine Learning Engineer designing and training lightweight ASR models for mobile devices at Plaud. Contributing to optimization, multilingual data management, and deployment collaboration.
FR
Hybrid Master Thesis – Machine Learning for Retired Lithium-Ion Cell Sorting
Posted yesterday
About the role
- Master Thesis focusing on developing machine learning models for lithium-ion cell sorting at Fraunhofer LBF. Involvement in innovative projects addressing circular economy in battery recycling.
Responsibilities
- 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
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