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a relevant PhD/DPhil (or near completion) in Computer Vision Or Machine Learning. Strong publication record at the principal international computer vision and machine learning conferences. Sufficient
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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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system or electrochemical system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be
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system or electrochemical system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be
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system or electrochemical system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be
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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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, 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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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 5 hours ago
cutting-edge machine (deep) learning numerical methods for wave-equation-based processing, imaging, and inversion. Wave phenomena are ubiquitous in science, and they extend to objectives ranging from
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strong background in geomodelling, geophysical and geotechnical investigation, geomechanical engineering, and machine learning. You will be expected to work effectively on a geophysical/geomechanical
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– Discovering Rare Proteins Across the smORF Landscape See advertisement About the position Would you like to use large-scale data analysis and machine learning to uncover proteins that conventional