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Description In this project, we develop machine learning models for prediction of optical properties of chiral molecules based on DFT/CCSD data which we calculate ourselves. We include derivative information by
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oriented, regionally anchored top university as it focuses on the grand challenges of the 21st century. It develops innovative solutions for the world's most pressing issues. In research and academic
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and training provision within CAFE-BIO are available from the network website ( https://cafe-bio.org ) and the official EU page for the network ( https://cordis.europa.eu/project/id/101226762
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response and how to leverage this knowledge to develop strategies for sustainable disease resistance in crops. Plants have evolved diverse immune receptors to perceive biotic stresses and trigger defence
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is to train creative, responsible and self-confident young researchers. The relevantproject focusses on the economics of soil-transmitted helminthiasis control interventions. Infections due to soil
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projects range from the analysis of basic cellular processes to clinical translation, from the application of novel biophysical approaches to the development of new imaging-related techniques and compounds
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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for policymakers. Within the Marie Skłodowska-Curie project MiningBrines, we use our skillset to help develop a sustainable brine mining whole strategy that extracts and valorizes renewable energy, energetic gases
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position is the development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties. The research is embedded in the thematic context
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to circular economies. As part of this mission, we are offering PhD positions in the Simulation and Data Lab Digital Bioeconomy (SDL‑DBE). The SDL‑DBE develops and applies multiscale models, AI-enhanced