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(including neural quantum states), stabilizer and near-Clifford simulation, Gaussian/free-fermion methods, open-system dynamics (Lindblad master equations). - Machine learning for physical systems: deep
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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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to develop a personal research programme in observational or theoretical cosmology, with a particular emphasis on ultra-large-scale cosmology (including primordial non-Gaussianity and horizon-sized effects
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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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closely related quantitative field in hand by the start of the appointment. ● Demonstrated expertise in molecular dynamics simulation and enhanced-sampling techniques (e.g., Gaussian accelerated MD/GaMD
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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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studies of organic, organometallic, photoredox, radical, or homogeneous catalytic systems. Fluency with Python and modern scientific computing workflows; experience with Git, HPC clusters, SLURM, Gaussian
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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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of laser optics, including Gaussian beam optics, accousto and electro-optics. Experience with Rydberg systems. Experience automating control of experiments, such as with LabView and/or python software