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Field
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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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-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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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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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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explore relations between energy and non-classicality, both to provide new ways to characterize non-classical quantum states of light, and optimize processes to produce them. Context and goal of the project
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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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. Description: The transition in the Arctic from multi-year ice to first year ice raises the urgency of process understanding and modeling of sea ice formation/melt processes, including the interaction with
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performing organic synthesis of fluorophores and light-sensitive substances Experience working with computational chemistry software packages including Gaussian and Orca Additional skills in fluorescence and
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method) leveraging Gaussian process (GP) emulators to isolate sensitive parameters and optimize workflow computational costs. Calibrate and validate computational models against individual and population
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research will develop and apply novel Bayesian machine learning methods – in particular physics-informed Gaussian processes and/or neural operators– to build accurate probability density functions (PDFs