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between the University of Plymouth and Cornwall Partnership NHS Foundation Trust, starting in May 2026. About the role The purpose of the role is to develop and apply mathematical models and computational
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 42 minutes ago
carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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learning models will be employed to anticipate coverage changes and manage gateway handovers proactively. This predictive approach is intended to minimize packet loss, reduce latency, and ensure continuity
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learning models to predict ion-exchange isotherm parametersIntegration of predicted parameters into the CADET chromatography simulation framework Simulation and analysis of batch and gradient elution
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exploring the evolutionary trajectories of energetic frustration from ancestral to extant proteins, combining ancestral sequence reconstruction, structure prediction, and statistical energy models
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description CRSA - Postdoctoral research fellowship: “Developing Advanced Weather Prediction Models for Agricultural
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spectroscopy, especially applied to the analysis of lipids or oils. Experience in the application of chemometrics to develop predictive models Participation in competitive research projects related to the field
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a