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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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conduct research on 3D reconstruction, with a particular focus on developing advanced 3D Gaussian Splatting techniques for dynamic and streaming scenarios. The role will involve designing and implementing
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techniques e.g. Gaussian/Amber/Schrödinger/python/bash For more information, please contact Asst Prof Shao, Huiling ([email protected] ). We regret to inform that only shortlisted candidates will be
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, UAEs, GANs, diffusion models/flow matching, and Gaussian processes for realistic load, generation and voltage time series. You will embed physical constraints into generation: power-flow consistency
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for classifying user activity in office buildings using power consumption data, with a focus on probabilistic approaches such as Gaussian Processes that provide principled uncertainty quantification
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-Informed Machine Learning perspective [1]. The doctoral student’s thesis will focus on operationalising this perspective through the development of Biology-Informed Gaussian Processes (BioGPs
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. Non-classical quantum states, from non-Gaussian states of light like Schrödinger cat states to entangled states, are precious resources for quantum technologies. Beyond their potential practical use
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mainly rely on Poisson log-normal (PLN) models with Gaussian latent variables, in which the observed dependencies between species are directly interpreted as ecological interactions. Although these models
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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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identified by, a set of parameters. Such methods are a popular choice in quantum dynamics, where the wavefunctions of many-body systems are approximated by multiple Gaussians. The methodology of interest