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, agriculture-based net emissions technologies, such as uptake and storage by agricultural soils and bioenergy production, are recognized as important components of future emission pathways to limit global
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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individual rates of ageing. Role You will extend BrainAGE from global estimates to regional normative models using Bayesian regression and GAMLSS to derive age- and region-specific reference distributions
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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topics. Candidates should have some experience working with FPGAs as well as an understanding of computer networks. Experience with both RTL and HLS design is favoured. The ideal candidate would have some
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mathematical information science approaches, such as scientific machine learning. Potential research topics include, but are not limited to: (1) Bayesian estimation of 3D velocity structure models using ocean
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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of using Bayesian methods in both model development and fitting. Previous experience and knowledge of research methods and study design in clinical trials. Knowledge of Good Clinical Practice (GCP) in