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Field
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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analyze phenotypic, genotypic, and environmental data to build prediction models for key traits of blueberry such as flowering time, yield, and fruit quality. This position reports to Dr. Sushan Ru
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earth system model data, with an emphasis on Seamless System for Prediction and EArth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize elements
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have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding of neuroimaging data to predict subjective
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predictions, investigating the impact of density prediction errors on orbit prediction, assisting with proposal writing, mentoring undergraduate and graduate students in the group and writing journal and
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cardiovascular treatments. In this role you will apply both machine learning predictive modelling and human genetic analyses of non-coding regulatory sequences to identify cell-specific targets for coronary artery
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the United States as well as clinical imaging and testing data from Stanford. Project themes will include developing models using EHR data to predict outcomes in ophthalmology and glaucoma, as well as investigating
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, molecular dynamics simulations using ab initio and machine-learning potentials, and the development or application of machine-learning tools for feature extraction, property prediction, and inverse molecular
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NIH- and foundation-funded projects focused on treatment pathways, chronic pain phenotyping, pharmacoepidemiology, and biomarker-based prediction across autoimmune rheumatic diseases. This position
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as this is a principal goal of the Sharifi Laboratory. For example, we have shown that our discovery of a gain-of-function in a steroid-synthesizing enzyme is a predictive biomarker of poor outcomes