Offer 54 out of 79 from 18/11/22, 09:28


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

Research Assistant - salary grade E 13 TV-L Berliner Hochschulen

part-time employment may be possible

The Berlin Institute for the Foundations of Learning and Data (BIFOLD) at the TU Berlin (Prof. Klaus Robert Mueller) is looking for a scientist for an agility project in the field of machine learning and bioinformatics. The AP is carried out in close cooperation with the "Institute for Computational Cancer Biology" of Prof. Dr. Roland Schwarz, at the Cancer Research Center Cologne Essen, University Hospital Cologne, Germany.

The Schwarz group develops algorithms for investigating intratumoral heterogeneity and cancer evolution. Based on algorithms for the reconstruction of cancer evolution in the patient from somatic copy number aberrations (SCNAs) [1], a method for assigning SCNAs to individual parental haplotypes was recently developed and applied [2]. The aim of this project is the development of a new algorithm for calling haplotype-specific copy number variants from single-cell strand-seq [3] data and for haplotyping of germline variants using machine learning methods.

Working field:

We are looking for an enthusiastic and independent researcher in the field of machine learning and bioinformatics. To model sequential biological data probabilistic approaches that can model dependencies between random variables (e.g. Probabilistic Graphic Models) are suggested as a starting framework. Alternative approaches should be evaluated independently. One focus will be on the concrete modeling of biological properties and the implementation of efficient algorithms for inference on large amounts of data.


Successfully completed academic degree (diploma, master’s or equivalent; doctorate desirable but not required) in mathematics, physics, computer science or bioinformatics; Extensive experience in the field of statistical methods and machine learning is expected; prior experience with Probabilistic Graphical Models, Hidden Markov Models or Bayesian networks is desired; very good programming skills in Python, NumPy / SciPy, PyTorch / TensorFlow are essential; very good language skills in English and German required; Experience in analyzing genomic data and / or sequencing data is an advantage.

How to apply:

Please send your written application, stating the reference number, with the usual application documents to the Technical University of Berlin - Die Präsidentin - Faculty IV, Institute for Software Technology and Theoretical Computer Science, Machine Learning Group, Prof. Dr. Müller, MAR 4-1, Marchstr. 23, 10587 Berlin or by e-mail (a 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