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Postdoctoral Research Associate: Drug Development Targeting Neuroinflammation in Alzheimer's Disease
Internal Number: 6319382 Postdoctoral Research Associate: Drug Development Targeting Neuroinflammation in Alzheimer's Disease Postdoctoral Research Associate: Drug Development Targeting Neuroinflammation in
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Postdoctoral researcher in Environmental Analytical Chemistry, with a focus on non-target screeni...
the field of environmental analytical chemistry and will focus on technical and conceptual developments in non-target screening, primarily of per- and polyfluoroalkyl substances (PFAS). Building on previous
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the field of environmental analytical chemistry and will focus on technical and conceptual developments in non-target screening , primarily of per- and polyfluoroalkyl substances (PFAS) . Building on previous
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Postdoctoral Research Associate: An exploration of the relationship between funding and medal targets, and athlete experience. ( Job Number: 25000663) Department of Sport & Exercise Science Grade 7
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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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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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methodologies. Understanding of integrating Bayesian approaches in NN-based model Knowledge of model deployments to cloud platforms or past work with AutoML tools. Knowledge of MLFlow for maintaining model
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statistical analysis and modeling techniques such as Gaussian process modeling, data assimilation, and Bayesian analysis; and 4. Open-source scientific software development. Expertise in computational
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing