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Field
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models/flow matching, and Gaussian processes for realistic load, generation and voltage time series. You will embed physical constraints into generation: power-flow consistency (Kirchhoff's laws) as soft
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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
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and climate adaptation worldwide. Your research will develop and apply novel Bayesian machine learning methods – in particular physics-informed Gaussian processes and/or neural operators– to build
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Job related to staff position within a Research Infrastructure? No Offer Description Neural reconstruction pipelines (e.g. Gaussian Splatting) and 3D sensing technologies such as LiDAR and
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underwater vehicles. We will explore advanced non-parametric mapping techniques based on our prior work in the area of Gaussian Process based Simultaneous Localisation and Mapping (SLAM) and recent advances
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reconstruction and rendering (e.g., NeRF, 3D Gaussian Splatting, novel view synthesis) and on scene understanding and interaction. Develop generative AI for video and 3D/4D generation, world models, and vision
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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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communication, sensing and power. The role also includes validating these methods through simulations and physical robot experiments. The development of uncertainty aware methods, such as Gaussian processes with