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optimization modeling. An ideal candidate would use their foundational knowledge of various modeling methods to develop and apply cutting-edge machine learning and data analysis techniques for assessing and
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We invite applications for a postdoctoral researcher to join the UMLFF project at the University of Luxembourg. The project aims to develop the next generation of uncertainty-aware machine-learning
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). The project investigates how machine learning (ML) can be used to enhance the modelling of boundary layers in industrial CFD simulations, where complex geometries and computational constraints limit near-wall
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 3 hours ago
Position Information General Information Position Number POST40 Working Title Post-Doctoral Fellow-Machine Learning in Thermal-fluid Sciences Division Academic Affairs Department College
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on neurodegenerative processes and are especially interested in Alzheimer’s and Parkinson’s disease and their contributing factors. The LCSB recruits talented scientists from various disciplines: computer scientists
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the design, development, deployment and evaluation of NeoShield’s applied machine-learning systems, the machine-learning-driven Clinical Decision Support Algorithm for neonatal sepsis and the real-time ward
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About the project: From Brittle to Ductile: Machine Learning 3D Fracture Simulations for Extreme Environments Supervisor: Prof, James Kermode, University of Warwick Develop cutting-edge machine
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We use cookies which are necessary to make our site work. We may also use additional cookies to analyse, improve and personalise our content and your digital experience. For more information, see our Cookie Policy and the list of Google Ad-Tech Vendors . You may choose not to allow some types of...
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Machine Learning Methods for Enhancing Autonomy of Unmanned Aerial Vehicles in Wildfire Detection and Localisation School of Electrical and Electronic Engineering PhD Research Project Self Funded
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RAP opportunity at National Institute of Standards and Technology NIST Scientific machine learning methods for trustable accelerated materials characterization and design Location Material