Fraunhofer-Gesellschaft is seeking a candidate for a master's thesis position combined with a student assistant role within the 'Computer Vision and Machine Learning' group at Fraunhofer ISE. You will work on the thesis topic 'Exploring latent spaces of deep networks for fault analysis in solar cells,' developing AI models to analyze manufacturing relationships and improve sustainable solar cell production.
What You'll Do
- Develop AI models to derive meaningful representations from complex data.
- Identify connections between production data and measurement data.
- Evaluate various statistical analysis methods.
- Work with real data and handle outliers and pitfalls.
- Regularly interact with colleagues and present results.
What We're Looking For
- Study natural or engineering sciences, such as computer science, microelectronics, physics, or a comparable field.
- Experience in computer vision, representation learning, and statistical data analysis.
- Ability to contribute to a team and achieve goals in interdisciplinary collaboration.
- Plan and complete tasks independently and with high quality.
- Persistence and problem-solving skills when facing challenges.
- Good communication skills and ability to build trusting relationships.
- Proficiency with PyTorch and training AI models.
- Very good English skills, both spoken and written.
Nice to Have
- Knowledge in solar cell research.
- Experience developing own models and performing statistical data analysis.
Technical Stack
- PyTorch
Team & Environment
You will join the 'Computer Vision and Machine Learning' group at Fraunhofer ISE.
Benefits & Compensation
- Exclusive insight into daily life of research and development at a research institute.
- Opportunity to connect experimental work with theory.
- Guidance by scientists with feedback on progress.
- Experience working in a team.
- Flexible working hours tailored to your needs.
- Equal opportunities and space for diversity.
- After-work events and annual staff parties.
- Remuneration based on the degree of the academic qualification.
We value and promote the diversity of the competencies of our employees and therefore welcome all applications—regardless of age, gender, nationality, ethnicity and social background, religion, worldview, disability as well as sexual orientation and identity.
