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Field
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approaches to understand the interconnections between nutrition, health, food systems, and society. Our lab has pioneered genomics techniques that objectively track dietary intake across hundreds of plant and
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techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
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organoids Experience with cerebral organoid models (is preferred but not mandatory) Molecular and biological assays to characterize stem cells and extracellular vesicles ES4TERM group has long track record in
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the project lead peer reviewed outputs relevant to acoustic tracking and project modelling objectives, and other objectives as appropriate. About you The University values courage and creativity; openness and
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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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settings Experience developing deep learning models for real-time image/video segmentation, object tracking, 3D reconstruction, super-resolution. Have a passion on obtaining external funding and project
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assessment models, and statistical modeling in both frequentist and Bayesian frameworks • A solid track record of publications Appointment Type Restricted Salary Information Commensuate with experience Review
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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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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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-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