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therapeutics by protein design. This project will apply cutting-edge generative AI methods—including protein design, structure–function prediction, and multimodal learning—to develop and optimize a new
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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | 5 days ago
homogeneous catalysis Computational design of novel non-precious catalysts Assessment and development of newly designed workflows Set up of data-driven models to predict properties of catalytic reactions Your
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opportunity to be part of the BaySensAI project (https://go.ur.de/baysenseai ) and develop an AI-based prediction and monitoring system for biodiversity data as part of the Bavarian research network bayklif2
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and odor perception and food-derived proteins. The project aims to make use of the structural information for structure-based ligand design projects in order to develop prediction methods to identify
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, including rare and extreme events. Quantify flood risk and improve early warning predictability in out-of-distribution conditions (climate change, land cover changes), and use explainable and causal ML
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Max Planck Institute for Evolutionary Anthropology, Leipzig | Leipzig, Sachsen | Germany | 22 days ago
events. Humans use simulations not only to predict outcomes, but also to evaluate counterfactual outcomes in hindsight. Indeed, counterfactual simulation, that is, simulating what could have happened, has
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diseases (VBD) in relation to climate change, such as West Nile fever, Dengue fever, and Tick-borne diseases. His group develops and uses approaches for multi driver predictions using statistics and machine
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diseases (VBD) in relation to climate change, such as West Nile fever, Dengue fever, and Tick-borne diseases. His group develops and uses approaches for multi driver predictions using statistics and machine
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 27 days ago
to develop prediction methods to identify new food ingredients and flavor modulators. Key Responsibilities AI-based methodologies for protein structure prediction and conformational ensamble Molecular
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systematic uncertainty quantification framework for remote sensing data for bridges. This will be the basis for Bayesian machine learning approachesto predict bridge deformations and manage uncertainty