Postdoctoral Researcher in Atomistic Machine Learning

Technische Universität München
Published
WorkplaceMünchen, Bayern, Germany
Category
Position
MyTUM-Portal
Technische Universität München

12.06.2024, Wissenschaftliches Personal

We are looking for a Postdoctoral Researcher to join the AI-Based Material Science group in the Physics Department at Technical University Munich.

In this position, you will have a chance to make an impact on the green and digital transition by developing and applying tools from artificial intelligence (AI) to physics, chemistry, and materials science. Our objective is to facilitate atomistic materials science with machine learning to gain insight into fundamental processes on the atomic scale and accelerate materials development.

YOUR ROLE AND GOALS You will build, maintain, and apply machine learning techniques for atomistic problems in materials science. Techniques and problem settings range from machine-learned interatomic potentials to multimodal regression, generative models, and Bayesian optimization. You will also carry out electronic structure theory (e.g. density-functional or Green’s function theory) calculations to generate data for machine learning and investigate materials properties and processes on the atomic scale. You will manage large-scale supercomputer simulations alongside AI algorithms and data analytics tools, lead machine-learning software development and supervise team members.

YOUR EXPERIENCE AND AMBITIONS We welcome candidates with a PhD in computational physics, chemistry or materials science who are curious about applied machine learning in the natural sciences. Prior machine learning experience is required. We seek colleagues who enjoy coding, scripting and analytics, and are keen to push the boundaries of computational materials design. This project requires creative thinking and programming, as well as technical expertise in materials simulations and a broad understanding of electronic phenomena in materials. We further appreciate willingness to travel, teach and mentor, collaborate, and communicate science.

WHAT WE OFFER In the AI-Based Materials Science group, led by Prof. Patrick Rinke, we advance electronic structure theory and machine learning to pursue innovative applications towards future technologies. We are a multi-cultural and cross-disciplinary team, with complementary subgroups and talents. You will train in machine learning applications with experienced developers, meet our global network of collaborators, join us at scientific meetings, help us organize research workshops and get involved in academic and diversity outreach. We will help you grow a competitive and international career profile. You will also be part of the materials science ecosystem at TU Munich (e-conversion excellence cluster, Munich Data Science Institute, Atomistic Modelling Center) and join a vibrant community at the crossroads of AI research, physics, materials science, and renewable energy technologies. TUM has continuously been rated as one of the top universities in Germany and one of the best universities for studying physics in Europe, while Munich is among the cities with the highest quality of living worldwide.

READY TO APPLY?

If you want to join our community, please email your application to Prof. Patrick Rinke at Rinke.officenat.tum.de. The application material should include:

(1) CV including list of publications (2) A one-page proposal for an (imaginary) atomistic machine learning Project (3) Degree certificates and academic transcripts (4) Contact details of at least two referees (or letters of recommendation, if already available) The position will be filled as soon as a suitable candidate is identified. For additional information, kindly contact Prof. Patrick Rinke. TU Munich reserves the right for justified reasons to leave the position open, to extend the application period, reopen the application process, and to consider candidates who have not submitted applications during the application period.

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Kontakt: rinke.officenat.tum.de
In your application, please refer to myScience.org and reference JobID 2903745.