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analyze spatial and temporal datasets collected from UAS, satellite, ground-based sensors, and other sources. Integrate multi-source datasets and develop predictive models to support crop monitoring and
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—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive
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. Research activities may include common- and rare-variant association studies, survival and longitudinal analysis, multi-state disease modeling, polygenic risk prediction, gene-environment interaction, cross
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modeling—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model
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modeling—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model
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computing environments, workflow-management systems, containers, and/or software development practices. Experience with machine learning, predictive modeling, or artificial intelligence methods applied
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predict cancer progression. We use a combination of experimental and clinical data paired with computational analyses to model tumor evolution. We seek a postdoctoral candidate who will apply a combination
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computing, cloud-based computing environments, workflow-management systems, containers, and/or software development practices. Experience with machine learning, predictive modeling, or artificial intelligence
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental