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, and contribute to identifying tumor vulnerabilities that may become future therapeutic targets. What we offer: A dynamic and interdisciplinary research team with expertise in cancer biology, statistics
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: Mathematics, Mathematical Statistics and Computational Mathematics. The research at the Division of Computational Mathematics covers many different areas in numerical analysis, symbolic computations
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for variant effect prediction. Good knowledge of human prehistory and human evolution. Good knowledge of statistics. Training in bioinformatics, genomics, molecular genetics is advantageous. How to apply
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an asset. Additional qualifications Working knowledge in statistics and infection biology is highly appreciated. Part of the DDLS program, to be employed as a PhD student, the applicant must be
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. Candidates are further expected to have experience in processing and analyzing high-throughput genomic sequencing data and in statistical analysis. Previous experience with Drosophila melanogaster or other
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, engineering physics, biomedicine, or similar Documented skills in data-driven analysis (machine learning using python with TensorFlow, PyTorch, or similar) and computational statistics Specific knowledge of big
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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, probability theory, etc) A competence in quantitative topics equivalent to a mathematics, statistics, physics, computer science, or engineering degree is required (if your degree was not in one of these domains
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networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and