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Paul Goldberg and will work on the EPSRC-funded project “Driving Behaviour in Multi-winner Voting” within the Algorithms and Complexity Theory research theme. The position is for the two-month period of
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of Computer Science. The post-holder will report directly to Professor Paul Goldberg and will work on the EPSRC-funded project "Driving Behaviour in Multi-winner Voting" within the Algorithms and Complexity Theory
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aims to develop algorithms for discovering patterns that are robust to data changes. They are expected to co-develop the problem definitions, design reslience measures, co-developing and implementing
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demonstrations. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming
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Assistant (RA) or a Postdoctoral Research Associate (PDRA). The appointed candidates will support advanced research initiatives focusing on systems design, distributed systems, and algorithmic optimization
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will also investigate process optimisation algorithms, providing a route toward autonomous optimisation and ultimately enabling long term stable operation under variable load conditions. This post is
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deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical optimality with computational efficiency. Reinforcement Learning through Stochastic Control. We will
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, quantum science, condensed matter, materials, photonics and interdisciplinary applications of physical science. This role sits within a collaborative quantum science project at King’s on quantum algorithms
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award at FOCS 2019. These positions are supported by an ERC Advanced Grant (Distributed Quantum Advantage , 2026–2031) and a QuantERA grant (Quantum Network Algorithms , 2026–2029); in the QuantERA
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new technologies for genetic privacy, a problem requiring molecular and computational systems to be designed together rather than sequentially. The postholder will join an active team developing