Bachelor / Master Thesis: Generative Adversarial Networks

Your Job:

• As part of your thesis, you will develop solutions for generating material structures for desired material properties
  
• You investigate material data based on Generative Adversarial Networks and current Deep Learning methods
  
• You implement and evaluate your solution approaches prototypically in Python (PyTorch / TensorFlow)

Your Profile:

• You study data science, computer science, mathematics, materials science, physics, engineering or a related subject
  
• You have good practical experience in Python or Julia or C/C++
  
• You have good structured and analytical skills as well as a systematic, careful, independent, and reliable working method
  
• You enjoy to solve complex problems

Our Offer:

We work on the very latest issues that impact our society and are offering you the opportunity to actively help in shaping change. Here is what Forschungszentrum Jülich can offer you:

• Intensive supervision of your thesis by an research employee
  
• An exciting work with personal responsibility in the research field of machine learning for material sciences
  
• An interesting and relevant topic for your thesis with future-oriented themes
  
• Ideal conditions for gaining practical experience alongside your studies
  
• Interdisciplinary collaboration on projects in an international, committed, and collegial team environment
  
• Excellent scientific facilities and state-of-the-art technology
  
• Flexible working hours as well as a reasonable remuneration
  
• A creative working environment in a leading research institution, located on an attractive research campus of TZA Aachen https://go.fzj.de/TZA
  

In addition to exciting tasks and a collaborative working atmosphere at Jülich, we have a lot more to offer: https://go.fzj.de/benefits.

Place of employment: Aachen

Your application should include a short cover letter summarizing your relevant experience, a CV, and certificates of your academic degree(s) (including subjects taken and grades).

We particularly welcome applications from people from a diverse range of backgrounds (e.g. regardless of age, gender, disabilities, sexual orientation/identity, as well as social, ethnic, and religious background). We strive to offer a diverse and inclusive working environment in which people enjoy equal opportunities and are able to fulfill their potential.