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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 21 hours ago
; expertise in several of the following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and
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and deep learning methods for large-scale genomic, clinical, and imaging biobank data, with stable multi-year NIH support. The Zhi Laboratory has a sustained track record of methods development
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning
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, Chemistry, Physics, Applied Mathematics, Materials Science, Chemical Engineering, or a related technical field. Demonstrated research experience in machine learning or deep learning for scientific
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 5 hours ago
seabed sensing research project focused on characterizing the upper-layer geoacoustic properties of deep-water seafloors. The position will support research involving acoustic sensing technologies, data
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 7 days ago
-series. Experience exploring machine learning and deep learning techniques for geospatial applications is highly desirable to effectively engage with Earth observation foundation models. Technical
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(e.g., deep neural networks, surrogate modeling, reinforcement learning, scientific AI). Willingness to work on interdisciplinary problems at the intersection of AI, control, and physical sciences
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various concepts in welding and related mechanical engineering processes and cleaning existing datasets. Creating deep learning models. Using deep learning to create a robust model that predicts welding
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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, or a related STEM field. Experience with EHR data, machine learning or deep learning, natural language processing, medical imaging, or large language models (LLMs) is highly desirable. Familiarity with