Offer 413 out of 460 from 17/02/23, 12:27


Tech­ni­sche Uni­ver­sität Ber­lin - Faculty IV - Institute of Software Engineering and Theoretical Computer Science / FG Machine Learning (ML)

Research Assistant - salary grade 13 TV-L Berliner Hochschulen - For qualification

part-time employment may be possible

The Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin (Prof. Klaus Robert Mueller / Prof. Volker Markl) is looking for a research assistant in the field of machine learning and bioinformatics for an agility sub-project. The project will be carried out in close cooperation with the Zuse Institute Berlin in the working groups of Prof. Dr. Christof Schütte and PD Dr. Tim Conrad.

The groups led by Schütte and Conrad are, amongst other things, developing methods for analysing large bio-medical data sets and modelling dynamic systems such as those found in the social and life sciences. The aim of the project "Model-regularised Learning of Complex Behaviour" is to develop new approaches for modelling and predicting complex dynamic systems from few, multivariate and noisy observational data, based on machine learning approaches.

Working field:

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:

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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.

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