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effects on astronauts. Detailed analyses of such effects, and possible ways to shield and mitigate against them, are typically conducted by using environmental prediction in combination with Monte Carlo
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of the future will increasingly rely on integrated digital systems that combine practical expertise with real-time feedback and predictive insights. At BFFI, we aim to improve CEA system design and crop
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of the future will increasingly rely on integrated digital systems that combine practical expertise with real-time feedback and predictive insights. At BFFI, we aim to improve CEA system design and crop
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AI-based decision-making, learning approaches, and predictive capabilities to enable spacecraft to react proactively to events while reducing reliance on ground intervention. A key focus will be
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these operations, and then train transformer models to predict sequences of operations in unseen data. Please see Weindel, Borst, & van Maanen, eLife, 2025, Weindel, van Maanen, & Borst, Imaging Neuroscience, 2024
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with researchers across the consortium to predict and edit targeted gene promoters, to design relevant constructs for gene editing and to evaluate the gene-edited plants to establish this topic firmly into the plant
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antigens presented by MHC molecules. This curiosity-driven project develops cutting-edge AI technology for 3D TCR–pMHC modeling to improve neoantigen identification, TCR specificity prediction, and TCR
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cloud cover may amplify rather than mitigate global warming but the magnitude of this is highly uncertain. One of the greatest challenges in climate science is to predict how clouds in general, and
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Challenge: Predict long-term cardiac growth and remodeling in cardiovascular disease. Change: Develop multi-scale mechanobiological models for virtual human twins. Impact: Advance patient-specific
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implantation for tinnitus but also which factors may predict success. In addition to the clinical research question, this project offers opportunities for methodological research. We are investigating how IPD