Postdoctoral Researcher in Atomistic Machine Learning

vor 1 Monat

Munich, Bayern, Deutschland Technical University of Munich Vollzeit
Job Title: Postdoctoral Researcher in Atomistic Machine Learning

We are seeking a highly motivated Postdoctoral Researcher to join our AI-Based Materials Science group at the Technical University of Munich. The successful candidate will have the opportunity to contribute to the development and application of machine learning techniques for atomistic materials science.

Job Summary:

The Postdoctoral Researcher will be responsible for building, maintaining, and applying machine learning techniques for atomistic problems in materials science. This will involve carrying out electronic structure theory calculations to generate data for machine learning and investigating materials properties and processes on the atomic scale. The successful candidate will also manage large-scale supercomputer simulations alongside AI algorithms and data analytics tools, lead machine learning software development, and supervise team members.

Responsibilities:
  • Develop and apply machine learning techniques for atomistic materials science
  • Carry out electronic structure theory calculations to generate data for machine learning
  • Investigate materials properties and processes on the atomic scale
  • Manage large-scale supercomputer simulations alongside AI algorithms and data analytics tools
  • Lead machine learning software development and supervise team members
Requirements:
  • PhD in computational physics, chemistry, or materials science
  • Prior machine learning experience
  • Strong programming skills in languages such as Python or C++
  • Excellent communication and teamwork skills
What We Offer:

The successful candidate will join a dynamic and interdisciplinary research group at the Technical University of Munich, with access to state-of-the-art research facilities and a collaborative and supportive work environment. We offer a competitive salary and benefits package, as well as opportunities for professional development and growth.

How to Apply:

Applications should include a CV, a cover letter, and contact details for at least two referees. Please submit your application to Prof. Patrick Rinke at. We look forward to hearing from you

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