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An exciting opportunity has arisen for a talented computer scientist to join our team as a researcher within the Green Algorithms Initiative in the Department of Public Health and Primary Care, one
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variants of importance sampling. We will connect these methods to modern formulations of Monte Carlo algorithms to improve their accuracy, scalability, and overall computational cost. The methodology so
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. These problems have been compounded by the emergence of Artificial Intelligence. New forms of algorithmic manipulation have been used to sow discord in democratic societies, undermine trust in politics, and erode
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of advanced computational techniques. This research will integrate power system modelling, optimisation algorithms, and artificial intelligence (AI) techniques to develop an innovative framework for strategic
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formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context
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-driven algorithms which can solve state estimation problems in fluid mechanics, such as inferring the instantaneous state of a fluid’s velocity field from sensors embedded in its boundary. The research
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frameworks to ensure the developed processes are compliant, scalable, and environmentally responsible. Multiobjective optimization algorithms will be employed to balance key performance indicators such as
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control algorithms for whole system efficiency optimisation. Design and simulate power management circuits using tools like SPICE or MATLAB/Simulink. Prototype and test circuits with real energy harvesters
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photorealistic game worlds. To achieve this goal, we need advances in many areas, from light transport, sampling, geometry and material representations, and computationally efficient algorithms to display
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mapping using a team of highly mobile legged or legged-wheeled robotic platforms. The research will investigate advanced algorithms for multi-robot coordination, dynamic path optimization, and collaborative