In this project, it is planned to couple machine learning approaches, especially from the field Deep Learning, with (reduced) ODE models in the sense that the model becomes an integral part of the learning iteration. In this way, the training of the deep network can build on the available - but possibly small and incomplete - data, but is additionally regularised by the relevant physics. For many scenarios, these reduced (or coarsened) models are available. Although they are significantly less complex and often based on only a few basic structural properties, they still contain the basic physics or structure of the problem. The developed methods will then used to analyse real-world data, e.g. data on opinion formation in social networks, on the spread of infectious diseases or from the field of single cell analysis. Teaching tasks.
In this project, we expect independent and self-motivated research in the described and also related areas. The major focus will be on the concrete modelling of the specific application-driven questions and the implementation of efficient algorithms for inference on large data sets.
Successfully completed university degree (Master, Diplom or equivalent) in mathematics, physics, computer science or bioinformatics; experience in the field of dynamical systems or ODE-based modelling and machine learning; very good programming skills in C/C++, Java or Python, especially with libraries such as NumPy/SciPy or PyTorch/TensorFlow. Experience in analysing data from the social or life sciences is an advantage. The ability to teach in both German and English is required.
How to apply:
Please send your written application, quoting the reference number, with the usual application documents 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: firstname.lastname@example.org
Application documents sent by post 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.
Technische Universität Berlin - Die Präsidentin - Fakultät IV, Institut für Softwaretechnik und Theoretische Informatik, FG Maschinelles Lernen, Prof. Dr. Klaus-Robert Müller, Sekr. MAR 4-1, Marchstr. 23, 10587 Berlin