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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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-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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and function in a collection of maize germplasm exhibiting diverse levels of yield stability under drought stress. The high-level objective of the research is to characterize the processes of water
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microbiota composition and function in a collection of maize germplasm exhibiting diverse levels of yield stability under drought stress. The high-level objective of the research is to characterize
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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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Generation in Context: Object- and Interaction-Aware Generative AI Models of Language Use”. Preferred start date is 1 February 2026 but is negotiable. Job descriptionFace-to-face conversation is the primary
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Generation in Context: Object- and Interaction-Aware Generative AI Models of Language Use”. Preferred start date is 1 February 2026 but is negotiable. Job descriptionFace-to-face conversation is the primary
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demonstrated track record in vision science, neuro-imaging, neuro-ophthalmology and deep learning. 2) Using Advanced Imaging-derived Biomarkers to Predict Health Outcomes: Neurological problems, such as stroke
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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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MOFs and their hybrid materials. The primary objective is to explore the fundamental structure–property relationships that govern MOF vitrification and liquid-phase behavior, and to develop new MOF-based