Master's thesis "Representation Learning for Solar Cell Production Analytics"

The Fraunhofer-Gesellschaft (www.fraunhofer.com) is one of the world's leading organizations for application-oriented research. 75 institutes develop pioneering technologies for our economy and society – more precisely: 32 000 people from technology, science, administration and IT. They know: Anyone who comes to Fraunhofer wants to and can make a difference. For themselves, for us and for the markets of today and tomorrow.

As one of the world's largest solar research institutes, the Fraunhofer Institute for Solar Energy Systems ISE makes a significant contribution to sustainable, economical, secure, and socially just energy supply worldwide. Our goal is to advance the energy transition with concrete, actionable technological solutions—through excellent research results, successful industry collaborations, and spin-offs. To this end, we conduct research with around 1,300 staff in four focus areas: energy supply, energy distribution, energy storage, and energy use. The highly modern R&D infrastructure of Fraunhofer ISE, with 22,300 m² of laboratory space, enables top-level research at an international standard.

Be part of change

Do you want to actively shape the energy transition and develop the latest AI methods along the way? We are working on sustainable and economically viable production of solar cells by bringing AI into production. Transfer current artificial intelligence methods into application with us!

You support our group "Computer Vision and Machine Learning" in developing AI models and transferring them into production.

In the master's thesis "Exploring latent spaces of deep networks for fault analysis in solar cells," you analyze relationships in solar cell manufacturing using state-of-the-art representation learning methods and classical statistical analysis techniques.

To support our group "Computer Vision and Machine Learning," we are looking for a student assistant to start as soon as possible, with the opportunity to write a master's thesis, for the following tasks:

You develop AI models to derive meaningful representations from complex data.
You identify connections between production data and measurement data.
You evaluate various statistical analysis methods.
You work with real data and handle outliers and pitfalls.
You regularly interact with colleagues and present your results.

What you contribute

You study natural or engineering sciences, such as computer science, microelectronics, physics, or a comparable field.
You already have experience in the areas of computer vision, representation learning, and statistical data analysis.
Knowledge in solar cell research is advantageous, but not required.
It is important to you to contribute to your team and to achieve goals together in interdisciplinary collaboration.
You plan and complete tasks independently and with high quality.
When facing challenges, you are persistent and do not give up until you achieve the desired results.
You find it easy to build and maintain trusting relationships. You express your ideas clearly and listen attentively to others.
In pursuing goals, you overcome obstacles and setbacks.
Proficiency with PyTorch and training AI models is natural for you.
Ideally, you have already developed your own models and performed statistical data analysis.
You have already demonstrated very good English skills, both spoken and written.

What we offer

Exclusive insight: In collaboration with the scientists of our research unit, you gain an insight into the daily life of research and development at a research institute.
Research mix: You will have the opportunity to connect experimental work with theory, applying and expanding what you have learned in your studies.
Supervision: During your work, you will be guided by scientists and receive feedback on your progress.
Teamwork: Through interaction with scientific and student staff, you gain experience working in a team and can contribute your existing experience.
Working hours and location: We offer you the option to flexibly tailor your working hours to your needs in consultation.
Equal opportunity: We value equal opportunities and create space for diversity.
After Work: Celebrate yourself and your colleagues at after-work events or our annual staff parties.

In addition to the master's thesis, a contract as a Research Assistant will be agreed upon. Remuneration is 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. Severely disabled people will be given preference if equally qualified.

Ready for change? Then apply now with your compelling application documents (including résumé, cover letter, and references/performance records) and make a difference! After your online application is submitted, you will receive an automatic acknowledgment of receipt. We will get in touch as soon as possible to tell you how things proceed.

Questions about this position will gladly be answered by:

Dr. Matthias Demant

+49 761 4588-5651

Fraunhofer Institute for Solar Energy Systems ISE

www.ise.fraunhofer.de

Requisition Number: 83002 Application Deadline: 02/28/2026

Job Segment: Statistics, Sustainability, R&D Engineer, Research Assistant, Data, Energy, Engineering, Research

Apply now »