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to the organisational, human and technology aspects of Cyber Security. The mission of the RMIT University Centre for Cyber Security Research and Innovation (CCSRI) is to be acknowledged as a world-class research centre
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Your Job: At the Institute for Advanced Simulation – Data Analytics and Machine Learning (IAS-8) we are looking for a PhD student in machine learning to work within a project linked to the
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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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Engineering Development Center, ORNL’s other nuclear facilities, and an assemblage of world-leading scientists and engineers. Please visit https://www.ornl.gov/directorate/isotopes for more information about
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
candidate will join a vibrant research environment on the UNC Chapel Hill campus, collaborating closely with scientists, engineers, technicians, and students at UNC and Triangle Universities Nuclear
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Your Job: At the Institute for Advanced Simulation – Data Analytics and Machine Learning (IAS-8) we are looking for a PhD student in machine learning to work within a project linked to the
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, earth sciences, energy systems, or material sciences University degree (M.Sc. or equivalent) in applied mathematics or in computational engineering science, computer science, simulation science with a
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for a highly motivated PhD candidate to join our world-leading research program in Earth System modelling and improving Earth System Modeling by better merging of measurement data and model simulations
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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modelling and improving Earth System Modeling by better merging of measurement data and model simulations. This PhD project focuses on improving how we estimate key parameters in land-surface and ecosystem