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

Posted 41 minutes ago

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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

Job title

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

Job type

Experience level

Mid levelSenior

Salary

Not specified

Degree requirement

No Education Requirement

Tech skills

Location requirements

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