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Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
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benefits , including medical, dental, and vision. To learn more about the Center on Race, Inequality, and the Law, visit http://www.law.nyu.edu/centers/race-inequality-law . Questions may be addressed
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, for up to two adoptions in your household. To learn more, please visit: https://www.hr.upenn.edu/PennHR/benefits-pay
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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop
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modeling and model–data fusion techniques, and developing faster, machine-learning–based tools that can stand in for slow model simulations. These tools will be used to test how model parameters influence
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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University of California, San Francisco | San Francisco, California | United States | about 1 month ago
(e.g., liberal arts, economics, public policy, and/or pre-medical background) and / or equivalent experience / training Skills to learn organization-specific and other computer application programs Basic
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. Qualifications Minimum: Master’s degree in engineering or a related discipline (e.g., Mechanical, Electrical, Computer, Energy, Materials, Mechatronics). Preferred: PhD’s degree in a relevant field. Prior