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optical setup construction. They will make use of commercial simulation software to test electromagnetic designs, algorithmic coding to design metamaterials, fabrication techniques to produce
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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optical setup construction. They will make use of commercial simulation software to test electromagnetic designs, algorithmic coding to design metamaterials, fabrication techniques to produce
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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic hardware. Both projects
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research group with a broad interest in plant biomechanics, ecology, development and evolution. A supervisory team comprising a plant scientist, a cell biologist and a physicist, as well as two postdocs with
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scientists, cell biologists, bioimaging specialists and physicists, as well as a postdoc with a specific background pitcher plant development, transcriptomics and bioinformatics. Supported by this expert team
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A position is available for a Postdoctoral Research Associate (Postdoc) in the forthcoming Aspirational Computing Lab in the Department of Computer Science and Technology at the University
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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