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, or simulation modeling to understand the functional roles of plant roots in agroecosystems. You have excellent communication skills and will to collaborate with researchers from different disciplines. You have
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analysis problems, especially tracking the motion of objects, which are driven by real applications in life science research Developing solutions to integrate large foundation models into microscopy image
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to investigate the influence of glucosinolate-based amines on the formation of Maillard-like products in model systems and foods, to isolate and identify the resulting products and to find out whether
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approaches across a range of model organisms to understand how and why we age. As a PhD candidate at FLI, you’ll be part of an international and interdisciplinary environment where basic science meets
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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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data Research experience with various demographic models (e.g. msprime, ABC or RELATE) Research experience in the denovo assembly and scaffolding of high-quality genomes using PacBio and Hi-C data
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and histopathological characterization of tumor cells in tissues using chemical and fluorescence staining Handling in vivo and in vitro models to test research hypothesis and refine study designs
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applications of disabled candidates. They will be preferred in case of equal qualification. We welcome applications from all backgrounds. For more information please contact: Dr. Sigrid Milles; e-mail: milles
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same aptitude, skills and professional qualifications. If you are interested, please send your complete and informative application documents (including references and a letter of motivation) by 30
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Senckenberganlage 25 60325 Frankfurt a.M. E-Mail: recruiting(at)senckenberg.de For scientific inquiries regarding the position please contact Dr Gabriel Ferreira. E-Mail: gabriel.ferreira(at)senckenberg.de Join our