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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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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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variety of perspectives enrich our work and our daily collaboration. In a continuous process of learning and reflection, we aim to ensure that all our employees can be themselves and feel a sense of
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environment that provides equal opportunities. We are convinced that diverse teams and a variety of perspectives enrich our work and our daily collaboration. In a continuous process of learning and reflection
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Reference number: 2026-0013 The Division of Computational and Molecular Prevention of the DKFZ, in collaboration with the Translational Molecular Imaging in Oncologic Therapy Monitoring Unit, invites
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biophysics, computational biology, mathematics in the life sciences, computer science and machine learning with application to biological systems, and related areas. What we provide The CSBD provides fully
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of Medicine as well as the University of Colorado Boulder. Scientists from your group will join cross-institutional lab meetings and have the option to visit collaborators on-site to acquire new technical
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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Computational Biology to join our multidisciplinary team. The position is embedded into a newly funded Collaborative Research Center (CRC) on Neurovascular diseases (CRC 1744) and further supported by
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mechanics, scientific computing and also a keen interest in interdisciplinary research and collaboration with experimental groups. PhD students hold (or expect to complete soon) a Master’s or equivalent