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Field
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enjoys a wide network of strong international collaborators all around the world, for example at the University of Oxford, the University of Melbourne, and the University of California, Los Angeles. We
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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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estimation methods for deep neural networks. A principled Bayesian framework for multimodal uncertainty modeling. Robust learning algorithms under missing modalities and distribution shifts. New uncertainty
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Advancing the state of the art in measurements of sound, vibration, force, acceleration and velocity
; Machine Learning; Artificial Intelligence; AI; PINN; Sensor Networks; Sensor Fusion; Optomechanics; Interferometry; Frequency Comb; Photonics; Acoustics; Sound; Sensing; Optics; Bayesian; Statistics; Signal
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of Bayesian approaches such as Gaussian process regression, particle filters, Bayesian networks, graph-based approaches. Probabilistic -based uncertainty quantification is also essential. Support the design
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data and data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal
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uncertainties. Knowledge of Bayesian approaches such as Gaussian process regression, particle filters, Bayesian networks, graph-based approaches. Probabilistic -based uncertainty quantification is also essential
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condensate dynamics [6] confirm that programmable multi-soliton architectures are now experimentally accessible. In par- allel, AI methods — reinforcement learning and neural- network-based optimization — have
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decision-making, reporting and robot dispatch in complex underground pipe networks. You will lead the design and evaluation of bio-inspired, optimisation-based and machine-learning-enabled methods for robot
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experiments remotely. SPEED is supported by collaborators at NC State, UNC–Chapel Hill, MIT, industry partners, and an initial national network of academic users. Wolfpack Perks and Benefits As a Pack member