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educational data science and machine learning in education are to be filled as soon as possible. The positions are funded through a Momentum grant of the VolkswagenStiftung and are embedded in the research
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, Heidelberg and Mannheim, our researchers harness interdisciplinary collaboration to decipher the complexities of disease at the systems level – from molecules and cells to organs and the entire organism
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considerations from a data analysis and instrument optimisation perspective. Teach users how to operate imaging devices Understand image formation processes to design methods for optimal information retrieval from
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transcriptomics, spatial gene expression, and biomechanical measurements. Implement physics-informed machine learning models to predict mechanical properties from cell morphology. Collaborate closely with
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representations • Research on multimodal representation learning for fusing heterogeneous clinical data (imaging, pathology, genomics, clinical text) into unified vector representations • Design and evaluation
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 8 days ago
backgrounds to collaborate and develop their skills to bridge molecular, cellular and organismal levels. You can learn more about our work . We seek candidates with a strong background in planning and
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economic modeling, with interests including improved spatial resolution and machine-learning-enabled approaches for policy analysis. Postdoctoral Position (f/m/d) – Integrated Assessment Modeling (Climate
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several historical, epistemic, and conceptual challenges of herbarium research in light of current digitization trends. BOTLEG will collaborate with botanists and herbarium experts in Central America
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strong foundations in machine learning and artificial intelligence, as well as a solid mathematical background. The position requires a strong interest in exploring multiple research directions toward
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for integrating expert data. Collaboration in the JOSHUA project to incorporate interdisciplinary, domain-specific knowledge into AI-supported crisis early warning and scenario simulation. Participation in PRIF’s