Master Thesis – Machine Learning for Retired Lithium-Ion Cell Sorting

Fraunhofer-Gesellschaft

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.

Posted 4/19/2026internshipDarmstadt • 🇩🇪 GermanyMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningPythonelectrochemistrybattery technologymodel trainingmodel evaluationdata analysisEIS-based analysiscell sortingliterature review

Soft Skills

strong interest in machine learningwillingness to learn