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testing (e.g. cell culture and organoid models). As part of a third-party grant from the Federal Ministry of Education and Research, the BfR is working in collaboration with the University of Veterinary
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conservation practices": PhD student (f,m,div) in the Field of Geodata, Nitrogen and Soil Parameter Modelling Reference number: 18/2025/4 The salary will be based on qualification and research experience
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Malignancies: From Mechanisms to Therapies”. In this CRC we will focus on myeloid malignancies as a model to dissect the various molecular mechanisms that enable and regulate cancer cell plasticity in AML. Our
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-management and conservation practices": PhD student (f,m,div) in the Field of Geodata, Nitrogen and Soil Parameter Modelling Reference number: 18/2025/4 The salary will be based on qualification and research
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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to model and analyse the intrinsic complexities of these systems. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains
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“Cellular Plasticity in Myeloid Malignancies: From Mechanisms to Therapies”. In this CRC we will focus on myeloid malignancies as a model to dissect the various molecular mechanisms that enable and regulate
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optimization or discrete algorithms. Profound mathematical modeling and programming skills. Experience with the design and analysis of graph algorithms or multiobjective optimization models is a plus. Very good
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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