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research themes combining theoretical analysis, probability, and computational modelling. Core duties include: Researching the non-local geometry and topology of Gaussian random fields, and random Laplace
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in the area of probabilistic methods in machine learning. These include applications of Random Matrix theory and Gaussian processes to Deep Neural Nets. Teaching may be required. Education and
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difficult to access on near-term quantum hardware. The difficulty is that the relevant Hilbert spaces grow rapidly, the dynamics are non-Gaussian and nonlinear, and the circuits must be mapped carefully
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and Gaussian state formalism. Quantum information theory and entanglement theory. Theory of spin-mechanical systems. Open quantum systems, continuous quantum measurement and feedback, and stochastic
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candidate with a disability, we are committed to ensuring fair treatment throughout the recruitment process. We will make adjustments to support the interview process wherever it is reasonable to do so and
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process. We will make adjustments to support the interview process wherever it is reasonable to do so and, where successful, reasonable adjustments will be made to support people within their role. Contact
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Probability, Gaussian processes, discrete probability, extremal combinatorics Zlil Sela Geometric group theory, model theory Alexander Sodin Mathematical physics, spectral theory Evgeny Strahov Random
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Gaussian-process emulators for accelerating parameter estimation and uncertainty propagation Selective cross-scale evaluation using complementary ecosystem observations (e.g., experiments) to test how AI