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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 7 hours ago
Technology (KAUST) is looking for postdoctoral researchers in the area of computer vision, AI, and machine learning. The initial appointment will be for 2 years with a possible extension with a
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, numerical method, and data-driven/machine-learning approaches, including: · Numerical simulation of kinetic equations: fast algorithms with stability/convergence analysis; · Data-driven and machine
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Join the HJF Team! HJF is seeking Postdoctoral Fellow - Computational Biology & Machine Learning who will be responsible for initiating and carrying out research projects in collaboration with a
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conduct collaborative research at the intersection of non-canonical kinetic theory, non-conventional plasma turbulence modeling, high-speed flows, scientific machine learning for multi-physics problems
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Theory (DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area
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machine learning group on database set up CO2 removal process Job Requirements: Candidates with strong background in Materials chemistry/Physical chemistry Experience with MOF synthesis, energy storage
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machine learning group on database set up CO2 removal process Job Requirements: Candidates with strong background in Materials chemistry/Physical chemistry Experience with MOF synthesis, energy storage
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machine learning group on database set up CO2 removal process Job Requirements: Candidates with strong background in Materials chemistry/Physical chemistry Experience with MOF synthesis, energy storage
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to support the development of AI-driven and data-driven approaches for the discovery and design of functional materials. The role will involve the development and application of machine learning
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
characteristics, drug efficacy, and safety profiles, and how these relationships can be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2