Ph.D. students (f/m/d) in Physics-based Machine Learning

06.04.2023, Wissenschaftliches Personal

The Professorship for Multiscale Modeling of Fluid Materials at TUM Campus Garching is looking for two Ph.D. students (f/m/d) in Physics-based Machine Learning, 100% with starting date as soon as possible.

Scientific environment

The Multiscale Modeling of Fluid Materials group provides a unique interdisciplinary environment, integrating state-of-the-art machine learning, multiscale simulations, and statistical physics to understand and exploit soft matter systems. The group, led by Prof. Julija Zavadlav, is part of the School of Engineering and Design at one of Europe's top universities, the Technical University of Munich. For more information, visit www.epc.ed.tum.de/en/mfm.

Project description

The positions are offered in the context of an ERC Starting Grant project called ‘SupraModel: Peptide-based Su-pramolecular Co-assembly Design: Multiscale Machine Learning Modeling Approach.’ ERC Starting Grant is a highly competitive funding program by the European Research Council (ERC) to support ground-breaking research in Europe.

SupraModel proposes a novel computational framework that will enable a rational design of peptide-based materials used in emerging technologies ranging from drug delivery to soft semiconductor devices. The successful applicant will work with other project members to develop next-generation molecular models where deep neural networks predict molecular interactions. These models enable molecular simulations at an unprecedented accuracy, giving quantitative insight into physical processes at the nanoscale, and will be used to advance supramolecular peptide-based materials.

Your profile

The position is open to candidates holding (or who will hold) a M.Sc. degree in physics, engineering, chemistry, applied mathematics, or related fields. We are particularly interested in applicants with:

• knowledge and experience with machine learning and/or molecular simulations
  
• proficiency in programming (especially Python)
• fluent in spoken and written English(knowledge of German is not required)
  

Our offer

Join our young research group with a scientifically stimulating atmosphere and participate in cutting-edge physics-aware machine learning research! The position is available immediately and for a duration of three years. Salary is based on the Free State of Bavaria public service wage agreement (100%, TV-L E13, starting at a higher level 2). Additional funding is available for scientific equipment and conference travel expenses.

How to apply?

Please send your application or questions regarding the position by e-mail to [email protected]. The application should include (in one single PDF document): a cover letter stating your motivation and background for applying for this Ph.D. position, a CV, certificates, transcript of grades, and contact information of two references. Preference will be given to applications received before May 15th, 2023.

TUM is an equal opportunity employer. TUM aims to increase the proportion of women, therefore, we particularly encourage applications from women. Applicants with severe disabilities will be given priority consideration given comparable qualifications. Data Protection Information: As part of your application for a position at the Technical University of Munich (TUM), you submit personal data. Please note our privacy policy in accordance with Art. 13 General Data Protection Regulation (DSGVO) http://go.tum.de/554159 for the collection and processing of personal data in the context of your application. By submitting your application, you confirm that you have read the privacy notice of TUM.

Data Protection Information:
When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

Kontakt: [email protected]

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