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FR
Fraunhofer-Gesellschaft
11d ago

Master's thesis "Representation Learning for Solar Cell Production Analytics"(m/w/x)

Freiburg im Breisgau
Full-timeInternshipOn-site
Green Job
AI/ML
Data Science

Description

You will drive the energy transition by developing AI models and representation learning methods to analyze solar cell production data, collaborating closely with a specialized research team.

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Requirements

  • Studies in natural or engineering sciences, computer science, microelectronics, physics, or comparable field
  • Experience in computer vision, representation learning, and statistical data analysis
  • Knowledge in solar cell research (advantageous)
  • Commitment to teamwork and interdisciplinary collaboration
  • Independent and high-quality task completion
  • Persistence and result-orientation when facing challenges
  • Ability to build trusting relationships and clear communication
  • Resilience in overcoming obstacles and setbacks
  • Proficiency with PyTorch and training AI models
  • Experience developing own models and statistical data analysis (ideal)
  • Very good spoken and written English skills

Education

Bachelor's degree

Tasks

  • Develop AI models for complex data representations
  • Identify connections between production and measurement data
  • Evaluate various statistical analysis methods
  • Process real-world data including outliers and pitfalls
  • Interact regularly with the research team
  • Present research results to colleagues
  • Analyze solar cell manufacturing using representation learning
  • Apply classical statistical techniques to fault analysis

Tools & Technologies

PyTorchAI models

Languages

EnglishBusiness Fluent

Benefits

Other Benefits

  • Exclusive research insights
  • Teamwork experience

Diverse Work

  • Experimental and theoretical work

Mentorship & Coaching

  • Scientific supervision and feedback

Flexible Working

  • Flexible working hours

Informal Culture

  • Equal opportunity environment

Team Events

  • After-work events
  • Annual staff parties
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