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Field
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graphs aims at exploiting the potential of the growing amount of structured data in all these areas to automate, accelerate and improve decision making. Analysing graph data requires solving problems
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to accelerate interventions capable of counteracting current and emerging climate threats. Autonomous robotics and associated digital systems are advancing rapidly across terrestrial, airborne, and aquatic
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to automate, accelerate and improve decision making. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics. Your future tasks: You actively
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Computer Science Northeastern University London As part of a major investment, Northeastern University London (NU London) has multiple, fully-funded PhD studentships available to accelerate its interdisciplinary
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transport within PWR secondary circuits. You will: Characterise particulate iron and corrosion products. Develop a laboratory flow loop to simulate secondary side flow conditions. Perform accelerated FAC
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of the growing amount of structured data in all these areas to automate, accelerate and improve decision making. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory
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About the project: Supervisor: Professor Nicholas Hine, University of Warwick This project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell
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Network Priorities This project strongly aligns with the Network’s first cohort priority themes: Accelerate Home Supply: By developing a model for incremental, small-scale additions within existing suburbs
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, a task essential for grid stability. The goals of the project are twofold. The first goal is to accelerate the solution of the large mixed-integer optimisation problems required to balance energy
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Your Job: You will be part of a research team that applies high-throughput experimentation to accelerate research in the emerging field of electrocatalysis. Catalyst inks are at the forefront