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plan that focuses on the development and combination of 3D-generative models and potency predictions for drug design. A successful research proposal to our question will focus on these topics: How
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-cell interaction proteomics by proximity labeling, subcellular localization by spatial proteomics and fluorescence microscopy, and protein structure prediction by artificial intelligence and crosslinking
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the information encoded in our genome to better diagnose, treat, predict and prevent disease. From the individual patient with rare disease, to the many thousands affected by complex, widespread illness, we
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disease into specific subclasses. You will develop AI algorithms to train models that predict if individuals (from which we create circuits) are prone to develop disease and to identify conditions that have
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mechanism. Recent developments in protein structure prediction and protein de novo design have opened new possibilities for probing such mechanisms. The project will seek to use existing algorithms to new
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(e.g., including simulation, prediction). Enable the evolution of integrated data/models all along the Digital Twin lifecycle Ensure the consistency of integrated data and models with regard to potential
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, predict the future course of the disease, and uncover the mechanisms of resistance to treatment. We leverage spatial and longitudinal data from patients and patient-derived model systems, evolutionary
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and climatic change is a large uncertainty for ecosystems, crop productivity and climate predictions. To tackle this uncertainty, we combine: growth chamber experiments, samples from world-unique CO2
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, thermal analysis, materials property characterization (PPMS and MPMS), property prediction with theoretical tools. As a person, you are independent, reliable and possess good communication and interpersonal
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-based models for predicting ecosystem variables such as greenhouse gas fluxes. The research project will primarily target large-scale ecosystem analysis across the EU or the world. We expect that you will