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build a computational model of SIHUMIx to predict new interactions and how the community reacts to disturbances—predictions that will later be tested in bioreactor experiments This work will give us a
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, computational pathology, and spatially resolved multi-omics data. The system will leverage generative models like diffusion models and Variational Autoencoders (VAEs) to simulate phenomena and predict outcomes. A
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About the role The SalGo Team ( https://salgo.web.ox.ac.uk/ ) at the University of Oxford’s Department of Biology seeks a Postdoctoral Researcher to join the NERC Pushing the Frontiers project
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of emerging artificial intelligence (AI) applications and tools, with an interest in exploring their potential use in data analytics and predictive modeling. Ability to translate complex analytical concepts
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development of model predictive control algorithms for autonomous robots. Key Responsibilities: Development of model predictive control algorithms for autonomous robots Job Requirements: A Master degree in
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of cementitious binders, including blended cements. Develop curing-specific DoC prediction models and validate against experimental results. Determine the effect of controlled exposure conditions on the DoC
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, mediators, and health outcomes, with the application to chronic diseases such as diabetes and its complications. AI and time series modeling for wearable device data and other longitudinal health data
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self‑delivering superchaotropic covalent hybrids; (ii) performing systematic transport screening to identify key physicochemical parameters and build a cargo‑dependent predictive model for covalent
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 39 minutes ago
with experience in causal inference predictive modeling, and data linkages will be given preference. Preferred candidates will have a strong publication record for their career stage, strong oral and
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, better adapted individuals can be selected at the seedling stage using only genetic data, accelerating the breeding cycle. Incorporating information about plasticity can aid genomic prediction modeling