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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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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
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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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biology to characterize carcinogen DNA damage and molecular signatures in the oral mucosa of smokeless tobacco users. This unique project will build on our deep understanding of tobacco-induced cancer
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research Experience with machine learning, deep learning, or multimodal data analysis Experience conducting human-subject research and IRB-compliant studies Experience working with healthcare systems
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in our collections or on view in our galleries. We seek an individual with a passion for teaching and interdisciplinary thinking; a deep commitment to object-based learning and inquiry; and a
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on machine learning, deep learning, control systems, sensing systems, and related technologies. Supports the development, testing, and transition of emerging autonomous technologies through laboratory
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National Energy Technology Laboratory (NETL) | Albany, New York | United States | about 12 hours ago
and deep learning-based digital twins. Stipend: The selected faculty participant will receive a monthly stipend commensurate with their institutional salary. Program Requirements: To document
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, and industry research programs. This is an opportunity to build deep scientific expertise while developing skills relevant to careers in academia, biotechnology, genomics, diagnostics, and clinical
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. You are expected to bring deep mechanistic insight, drive the scientific narrative, and leverage our robust pre-clinical modelsâ”complemented by our clinical trial multi-omic dataâ”to build your own