Independent and responsible research in the field of machine learning. The goal of the advertised project is the development of scalable methods for modeling atomic interactions in quantum chemistry. The focus is on dimension reduction of partial differential equations to describe essential properties of electronic systems at the atomic level.

The associated tasks are:

• Development and implementation of ML models in quantum chemistry;
• Incorporation of physical knowledge about the target systems into the models, e.g., distinguishing different kinds of short- and long-range interactions between atoms;
• Evaluation of the developed methods using benchmark problems and contextualizing observations within physical theory;
• Communication of results through presentations.
• teaching tasks

• Successfully completed university degree (Diploma, Master, or equivalent) in Physics, Mathematics, or Computer Science
• Proven experience in machine learning with a strong understanding of algorithms, linear algebra, functional analysis, and related mathematical concepts
• Solid knowledge in physics, including methods for solving the Schrödinger equation
• Excellent programming skills in Python and solid knowledge of common machine learning frameworks such as PyTorch or TensorFlow
• Strong communication skills in English and the ability to explain complex topics to a broad audience with diverse backgrounds (i.e., both computer scientists and physicists)
• Familiarity with state-of-the-art machine learning models and approaches for modeling molecular force fields with ML
• The ability to teach in German and/or in English is required; willingness to acquire the respective missing language skills

Desirable.

• The ability to interact with a team of ML experts and physicists.

Please send your written application, quoting the reference number, with the usual application documents (i.e. at least cover letter, CV, graduation certificates, grade overviews, etc.) to Technische Universität Berlin - Die Präsidentin - Fakultät IV, Institut für Softwaretechnik und Theoretische Informatik, FG Maschinelles Lernen, Prof. Dr. Müller, MAR 4-1, Marchstr. 23, 10587 Berlin or by e-mail (one PDF file, max. 5 MB) to: jobs@bifold.berlin.

For cost reasons, application documents sent by mail will not be returned. Please submit copies only.

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To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities. pplications from people of all nationalities and with a migration background are very welcome.