Technische Universität Berlin offers an open position:
part-time employment may be possible
The Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin (Prof. Klaus Robert Müller) is seeking a Research Associate in Machine Learning for an Agility subproject. The agility project will be carried out in the research groups "Machine Learning for Molecular Simulation in Quantum Chemistry” (https://www.bifold.berlin/people/dr-stefan-chmiela.html) led by Dr. Stefan Chmiela and "Probabilistic modeling and inference" (https://web.ml.tu-berlin.de/author/dr.-shinichi-nakajima/) led by Dr. Shinichi Nakajima.
Dr. Chmiela’s team is engaged in the modeling of multi-body systems with applications in quantum chemistry, particularly for predicting solutions to the Schrödinger equation. The overarching goal of the team is the development of models to accelerate accurate molecular dynamics simulations for the calculation of dynamic and thermodynamic observables of physical systems.
Independent and responsible research in the field of machine learning. The goal of the advertised project is the development of new explanation methods (Explainable Artificial Intelligence, XAI) for atomistic modeling in quantum chemistry. The focus is on acquiring physically grounded insights to guide hypotheses in quantum chemistry. The associated tasks are:
Desirable:
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.
By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guarantee for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the TU staff department homepage: https://www.abt2-t.tu-berlin.de/menue/themen_a_z/datenschutzerklaerung/ or quick access 214041.
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. Applications from people of all nationalities and with a migration background are very welcome.
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