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Field
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(that might predict learning and outcomes), underlying cognitive and neurobiological mechanisms, and intervention outcomes using tools including, but not limited to brain imaging (broadly defined
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clear, auditable explanations of assessments and predictions, ensuring analysts and operators understand model outputs in time-sensitive security scenarios. Real-time intelligence fusion and rapid
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to advance predictive modeling frameworks for soft, rate-dependent materials under complex thermo-mechanical loading conditions. The research will involve close integration of analytical constitutive modeling
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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experimental and computational datasets. The overarching objective of this work is to establish predictive, patient-specific models capable of forecasting clinical outcomes in breast reconstruction, thereby
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understanding of the underlying physical mechanisms and to leverage this knowledge to develop predictive tools for optimizing the design and control of wind farms. Research scope and responsibilities Depending
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, Python, and SAS or Stata) • Demonstrated expertise in causal inference and high-dimensional risk adjustment/predictive modeling, experience with Medicare claims data • Clear scientific writing and
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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, but also to develop approaches to prevent damage. In the project we aim to develop a numerical model to predict root growth. Your task as a Postdoc researcher is to develop a measurement and monitoring
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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