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Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 1 month ago
. The successful candidate will integrate multiscale first-principles modeling, sparse symbolic regression, and adaptive experimental design in self-driving laboratory settings to elucidate both desired catalytic
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become part of an exciting project at the Senckenberg Museum of Natural History Görlitz (Saxony, Germany). We are looking for a motivated environmental data modeler – data scientist (m/f/d) to support the
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preclinical in vitro and in vivo models Supervise and mentor MSc/MD/PhD students Collaborate with national and international research partners Present your results at scientific meetings Publish your findings
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modeling. Our research contributes to robust management concepts for the sustainable provisioning of diverse ecosystem services, and for maintaining the integrity and diversity of ecosystems in a changing
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Junior research position or post-doctoral position (m/f/d)(Position number: 19-2025 Postdoc PREVENT) in the field of Weather Extremes and Climate Modelling, starting on 01.09.2025. The position is funded
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multi-disciplinary approaches to answer these key questions including; immunology, oncology (in vitro model-organoid systems, ex vivo tissue culture), microbiology, next generation sequencing (16S seq
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | about 1 month ago
A doctoral degree in neuroscience or a related STEM field Prior experience in systems neuroscience using animal models A solid foundation in quantitative data analysis and statistics Programming
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its role in hematopoiesis over life time. We plan to employ inflammatory challenges and will combine experimental animal models with studies on human cells to translate principles and mechanisms
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Design and implement optimization techniques for full-stack improvement of quantum algorithms Model major sources of experimental error for control theory or for error mitigation techniques Scientific