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Student Assistant in Computational Biology

Ludwig-Maximilians-Universität (LMU) München is recognized as one of Europe's premier academic and research institutions. The Gene Center is a central scientific institution of the LMU located in the heart of its Life Science Campus in Großhadern/Martinsried.

The research group of Simon Mages at the Gene Center, LMU Munich, Germany, is looking for a

Student assistant (f/m/d) in computational biology

Our research

We are working at the intersection of basic biology, high-performance computing, data science, and clinical application to strengthen the understanding of the interactions of the many actors in living tissues and to provide a better basis for the understanding of diseases. For this we develop and apply methods to analyse the large and diverse molecular datasets which are routinely collected in basic and clinical research with a focus on single cell and spatial transcriptomics methods. A particular focus is the application and adaption of concepts from theoretical physics on question in biology and bioinformatics.
Find out more here: https://www.genzentrum.uni-muenchen.de/research-groups/mages/

Your qualification

  • Bachelor’s degree or equivalent qualification in physics, mathematics, informatics, data science, or a related field
  • Programming skills on a high level (Python) and on a low level (C/C++) or with machine learning frameworks (jax, pyro, Pytorch, Tensorflow)
  • Experience in working on computational science problems (e.g. lattice field theory, numerical solution of PDEs, multiplex imaging data)
  • Interest in immunology, cancer biology, or cellular systems/tissue architecture
  • Fluent written and spoken English

Our offer

You will be working in a multidisciplinary environment with an equal focus on scientific excellence and inter-personal competence. You will have the chance to explore a dynamic field of research where the application of your computational skills can have a real impact. Tasks range from writing efficient code for the analysis of large spatial datasets to modelling derived spatial quantities with classical or machine learning methods. The start date is flexible.

Your application

Please send your application consisting of a cover letter and a curriculum vitae to mages@genzentrum.lmu.de

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