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. The appointed candidate will join an MRC-SA/MRC-UK funded project whose objective is to address and understand the role that the water, wastewater, animal waste systems and food, plays in the transmission
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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
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discipline knowledge and developments. Demonstrated ability to undertake high quality academic research and conduct independent research with limited supervision. Demonstrated track record of publications and
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: developing and testing new approaches to water resources modelling, application of Bayesian inference methods to environmental problems, machine learning and data science applications, undertaking analysis and
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critical questions about the ways in which AI tools work and how they might be misused or abused (both intentionally and unintentionally) Boundary objects that can be used to bridge the divide between
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for a Postdoctoral Scholar. The Scholar will conduct research on Bayesian spatiotemporal modeling methodology under the direction of Professor David Dunson at Duke on developing novel models motivated by
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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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(e.g. Python, R) Experience in the use of neuroimaging analysis (fMRI, MRI) to study mechanisms of brain function Previous experience of using Bayesian methods in both model development and fitting
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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
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Ability to acquire new skills beyond current expertise to achieve project objectives. In-depth theoretical and practical knowledge in engineering or cell biology. Ability to work independently and