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, metabolomics and bioinformatics tools for application in biology, medicine, plant and food research within TUM and research partners outside the university. For the bioinformatics branch of this core facility
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to 5 and more years. Requirements: • You have a PhD degree (or postgraduate degree MSc) in a computational discipline, preferably with significant experience in Bioinformatics or Computational Biology
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. In this position, you will drive the bioinformatical/computational aspects of collaborative projects aiming to ultimately improve immunomonitoring of cancer patients by providing a refined strategy to
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computer science, bioinformatics or related fields Solid understanding of machine and deep learning and relevant frameworks (e.g. Pytorch or Tensorflow, Keras, scikit-learn, OpenCV) Proficiency in Python, Linux and
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: 11.07.2025 Limitation:Temporary (2 Jahre, Verlängerung angestrebt) Contract:TV-L Your tasks Single-cell multiomics in primary cells: wet-lab experiments and performing bioinformatic analyzes on the single-cell
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potentially to assess leverage points identified in this study Your profile Master degree (or similar) in molecular biology, biomedicine, biotechnology, bioinformatics or a related field Experience in molecular
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– Integrative Genomics Core Unit (NIG), we are seeking a highly motivated scientific staff member with expertise in the bioinformatic analysis of next-generation sequencing (NGS) data. Your tasks development
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. The Leibniz-LSB@TUM comprises a unique and world-leading research profile at the interface of Food Chemistry and Biology, Chemosensors and Technology, and Bioinformatics and Machine Learning. Leibniz-LSB@TUM’s
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xenograft (PDX) models. To this end, the candidate will have direct access to cutting-edge mass spectrometry and advanced bioinformatics infrastructure. The project is part of the collaborative research
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sequencing, genetic modification, or protein biochemistry. You will work with bioinformatic tools and workflows to analyze sequencing results. Data analysis will include the use of statistical methods