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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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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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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big
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links) and Space Domain Awareness (tracking satellites, debris, and near-Earth objects). Expanding AO to these domains introduces challenges: extreme performance for faint or high-contrast targets
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Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object tracking, 3D reconstruction, super-resolution. Have a passion on
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variants on human traits and single-cell readouts. Our research group is pioneering computational methods for deciphering molecular variation across individuals, space, and time. We have a track record in
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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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) or other expertise relevant to the project objectives (see above) Early career track record including publications or other outputs in the related field In case your background is in psychology or medicine
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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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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