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combining artificial intelligence with human expertise. The lab develops knowledge-grounded artificial intelligence, text mining and knowledge assembly, mathematical and systems modeling, and causal analysis
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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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convection. The objective is to develop reduced but realistic models of cumulus life cycles that may be applied toward cumulus parameterization and/or machine-learning algorithms for predicting short-term
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fluxes and acidification in global marine P cycling through a combination of field work in the North Atlantic Ocean, laboratory experiments and numerical modelling. A scientific cruise with our research
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. The candidate will contribute to projects focused on developing advanced machine learning models to quantify phenotypic and agronomic traits of crops, including corn, soybean, and other selected species
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develop the urgently needed system understanding, data analysis tools, and forecasting models for science and expert users, and co-create common ground among stakeholders for future-proof coastal management
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 18 days ago
-sensitive modeling, high-fidelity experimental characterization, and uncertainty quantification capabilities for metallic aerospace materials, with emphasis on additively manufactured (AM) metals
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generalization. Your work will include: Developing methods for detecting the authenticity and origin of speech and audio content generated by multiple models and containing different watermarking methods. Creating
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language models (LLMs) and vision-language models (VLMs), you will investigate AI behavior, develop innovative evaluation methods, and translate scientific insights into real-world applications that improve
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gliomas. The project integrates generative AI, interpretable models, uncertainty estimation, and federated learning to improve clinical decision-making, data privacy, and personalized medicine