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., probability, analysis), eager to conduct cutting edge research in the field of uncertainty quantification, in particular the theory and methods known as predictive Bayes. Predictive Bayes theory involves
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Actionable Data for Opioid Response in KY (RADOR-KY) project. This position will build data science solutions and predictive models for time series forecasting systems related to risk prediction, outcome
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they change through time. To translate eBird observations into robust data products we create custom modeling workflows designed to fill spatiotemporal gaps based on remote sensing data while controlling
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, regulatory, or multimodal biological data. Support target and mechanism prioritization by integrating model predictions with biological knowledge and external data sources. Work closely with academic partner
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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. Experience with advanced analytics, including predictive modeling, data science, or statistical analysis to support data-driven decision-making. Demonstrated experience designing and implementing ETL/ELT
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to predict nitrogen (N) and phosphorous (P) excretion, and this was published by Fox et al. (2004). Further, those predictions were refined and improved and partition N and P excretion between urine and feces
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of pharmaceutical formulation and manufacturing processes. The role The post holder will develop and implement mechanistic models to analyse and predict the behaviour of pharmaceutical processes. Your work will
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bioinformatics for immunology research programs. You'll work at the cutting edge of AI-enhanced immunology, applying deep learning, foundation models, and advanced machine learning approaches to understand how
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at the intersection of mathematics, computation, and cancer biology. We develop mechanistic, predictive models of cellular decision-making to address fundamental and translational challenges in cancer, including drug