Technische Universität Berlin - Faculty IV - Institute for Software Engineering and Theoretical Computer Science / Chair Machine Learning
Research Assistant - salary grade E 13 TV-L Berliner Hochschulen
under the reserve that funds are granted - 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 "Evolutionary and Cancer Genomics" group of Dr. Roland Schwarz, at the Max Delbrück Center, Berlin Institute for Medical Systems Biology (MDC-BIMSB).
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), a method for assigning SCNAs to individual parental haplotypes was recently developed and applied. The aim of this project is the development of a new algorithm for haplotype-specific copy number variants and haplotyping of germline variants using machine learning methods.
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 completeted studies (Master, Diplom 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.
Please send your application with the reference number and the usual documents only via email (single pdf file, max. 5 MB) to email@example.com.
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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 - Der Präsident -
Fakultät IV, Institut für Softwaretechnik und Theoretische Informatik,
FG Maschinelles Lernen, Prof. Dr. Müller, Sekr. MAR 4-1, Marchstr. 23, 10587 Berlin
Technische Universität Berlin - Der Präsident - Fakultät IV, Institut für Softwaretechnik und Theoretische Informatik, FG Maschinelles Lernen, Prof. Dr. Müller, Sekr. MAR 4-1, Marchstr. 23, 10587 Berlin