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for: Analysis of phenotypic, microscopic, PET/MRI, MALDI-MSI, and LC-MS/MS data Development of AI-based models for personalized risk assessment Validation and application of model performance using established
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The Bernhard Nocht Institute for Tropical Medicine (http://www.bnitm.de/en ) is the largest Research Institute for Tropical Medicine in Germany and is the National Reference Centre for Tropical
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. For the Working Group “Ecosystems in Transition” in RD1 at PIK, we are offering a PhD position (m/f/d) (Position number: 10-2026 PhD ARTECO) in the field of ecological modelling of complex arctic landscapes
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Develop solutions to integrate large foundation models
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investigate chromatin control in 2D and 3D cell culture models (including human organoids) infected with human polyomaviruses, in particular BK-Polyomavirus (BKV) and Merkel Cell Polyomavirus (MCPyV). Identify
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a related quantitative field. You have hands-on experience in crop genomics and managing genotyping data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are
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of Bayreuth), Peter Biedermann (University of Freiburg), and a joint modelling project located at the Dirk Metzler group (LMU Munich) and Mark Harrison (Coventry University). The project is also part of
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leading to cell death upon pathogen recognition. A recent evolutionary model proposes that NLRs have evolved from single units (singleton NLRs) able to both detect and respond to the presence of pathogen
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metastatic lesions, and how these differences shape varying responses to immunotherapies. To achieve this, spheroid and organoid models will be combined with synthetic biology strategies and state-of-the-art