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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics
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-facing web GIS dashboard. Investigate the forest, landscape, and climate conditions that drive storm susceptibility, using major windstorms as natural experiments and interpretable machine-/deep-learning
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research career. Research and technical training Training will include hands-on work in the following areas: Developing and validating deep-learning and machine-learning models using echocardiography
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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analysis. ● Prior work on kinase or other signaling-protein conformational dynamics, phosphorylation-driven activation, or allosteric regulation. ● Familiarity with machine learning and deep learning
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deep learning, and its extensions for these additional targets. In particular, we have a large collection of newspaper articles dealing with migration-related topics, and we are investigating how text
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the eBird project, one of the largest sources of avian biodiversity information in the world. The eBird team is a collaborative and innovative group that includes staff with deep expertise in bioinformatics
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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modeling of post-tuberculosis lung disease risk using approaches including generalized linear models and deep learning. Performs other job-related duties as assigned. Minimum Qualifications MD or Ph.D. in