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limited understanding of how tumors systemically impair immune responses. Meanwhile, traditional animal models often fail to capture the complexity of human immune-cancer interactions. This position
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or equivalent and a PhD (or close to completion) in computer science, math or comparable, or an applied/life science (e. g. engineering, biology, medicine) with a focus on data analysis and/or machine learning
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evaluation methods for vision models Design metrics to compare neural and behavioral data with computational predictions Collaborate with CRC groups on open, reproducible standards for model evaluation Mentor
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 21 days 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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Max Planck Institute for Biology Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 1 month ago
once a year to meet with the consortium. The position is for two years, with the possibility for extension. Requirements PhD (or equivalent international degree) or enrolled in a PhD degree program (or
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The Leibniz Institute of Plant Biochemistry invites applications to fill the position of PostDoc Researcher for Metabolomic and Proteomic Analysis (m/f/d) (Salary group E13 TV-L,working hours 100
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We offer a postdoc position in a project aiming at the development of organoid models for inflammatory bowel diseases. The project is a collaboration between the Fraunhofer Institute
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participants, and mathematical / statistical modeling. Requirements for employment are a completed PhD degree in a relevant field (Linguistics, Cognitive Science, Psychology, Philosophy, or similar), near native
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CRISPR/Cas9, cloning, protein biochemistry, as well as work with cell cultures and primary patient samples (S1/S2) Conducting and establishing experiments with in vivo mouse models Analysis and
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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