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Field
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processes. The excellent outcrop exposure due to limited vegetation cover, and extensive mineral occurrences provide a unique natural laboratory to advance predictive mineral system models. Our research will
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! In this exciting role, you will leverage Artificial Intelligence,data science ,mechanistic models ,robotics , andsynthetic biology to enablequantitative predictions of biological systems and support
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apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high
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biocatalysis. The Research Fellow will help conduct large-scale bioinformatic, mechanistic, and structural analyses to elucidate the physical basis of enzyme activity; develop computational models that integrate
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solutions for offshore wind turbines, enabling to enhance their structural awareness, real-time reliability assessment, and predictive maintenance decision support through integrated sensing, modelling, and
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of innovative statistical methods for analysing large-scale genomic and single-cell omics datasets, identifying causal genetic variation, and improving phenotype prediction and gene prioritisation across
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other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases
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of unknown PFAS, supporting agricultural research and advancing the ARS mission. The project will involve compiling high-resolution mass spectrometry (HRMS) databases for PFAS, creating machine learning models
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 20 hours ago
curves with direct application to regulatory bioassay development and drug product quality control. Gain experience in building, selecting, validating, and calibrating predictive models appropriate
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of their development and structure. Working closely with colleagues at the Universities of Leeds and Reading, you will integrate theoretical understanding, observational data, and modelling approaches to improve