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Immunology and Computational Biology Reference number: 2025-0105 We are deploying advanced in vitro and in vivo model systems, genetic perturbations and single cell technologies with spatial readouts to study
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within the Clusters of Excellence ‘Machine Learning for Science’ and ‘Image-Guided and Functionally Instructed Tumor Therapies (iFIT)’. Requirements PhD in Bioinformatics, Computational Biology, or a
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technology Strong mathematical education, in particular in relation to linear algebra Strong programming experience Ability to effectively communicate in written and spoken English Ability to work autonomously
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of chemistry, biochemistry or comparable Salary group 13 TVöD Temporary contract until 30.06.2026 under the reserve that funds are granted Full-time / suitable as part-time employment The Bundesanstalt für
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Postdoctoral Researcher (gn*) Life Science Reference Number: 10899 Fixed term of 3 years | Full time with 38,5 h | Salary Grade TV-L E13 | European Institute for Molecular Imaging We are UKM. We
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scientific journals and conferences Your Profile: A doctoral degree in Physics, Materials Science, Computer Science, Data Science, or related fields Proven experience with large language models (LLMs), natural
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Your Job: The research will be part of the recent ERC Starting Grant "Atomic Scale Quantum Sensing and Information with Molecular Nanostructures on a Scanning Probe Tip" (ERC-2024-StG, QuSINT). Our
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, epigenetics, cardiovascular science, computational biology, or a related field A strong background in chromatin biology, gene regulation, and/ or cardiovascular biology Prior experience with genomics
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Bioprogrammable Materials is looking for an enthusiastic Post-doctoral Researcher with experience in Protein Engineering. We combine synthetic biology and biomaterials to develop smart platforms for therapeutics
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: ausschreibung10-25@mpinat.mpg.de Apply now Max Planck Institute for Multidisciplinary Sciences Department of Theoretical and Computational Biophysics Prof. Dr. Helmut Grubmüller Am Faßberg 11 37077 Göttingen