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development, human-computer interaction, data analytics, user experience design, remote monitoring systems, energy optimization algorithms, and environmental impact modeling. Human-centric AI-driven sanitation
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physical laws, or an implicit form of extra data examples collected from physical simulations or their ML surrogates. In medical domains, patient data is typically distributed across multiple hospitals
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needs. By bridging human-centric innovation, generative algorithms, and sustainability metrics, this project seeks to redefine how novel products and systems are conceived, developed, and evaluated. You
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. This project seeks to advance energy autonomy by optimising power conversion, storage, and distribution in such systems, enabling broader adoption in real-world applications. The project aims to develop a PMC
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optimisation algorithms to dynamically reconfigure the substation/distribution network settings to enhance the system efficiency. The optimisation algorithms will incorporate the uncertainties associated with
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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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considered for this project. Application form Closing date 14 March 2022 Decision date May 2022 Additional information ¿Multi-criteria Automatic Algorithm Configuration under Streaming Problem Instances
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sensors, firmware-controlled automation, wireless connectivity, and maintenance algorithms. Students will design, build, and test smart sanitation solutions that can monitor system performance, optimize
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as to what role law should play in reducing potential harms, in helping to distribute risks and benefits across different groups in society, and in how existing (or future) legal rights and duties
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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