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for breast cancer screening.” Nature (2020). https://www.nature.com/articles/s41586-019-1799-6 (opens in new window) Dayan, I. et al., “Federated learning for predicting clinical outcomes in patients with
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
microstructure evolution during extrusion is critical for controlling final mechanical properties and surface appearance of extruded profiles, yet quantitative predictions remain challenging due to the complexity
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rapidly-updated archives of key weather observations; develop procedures for quality controlling these data archives; develop procedures for blending and interpolating in situ observations with remotely
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innovation within the disciplines of geotechnics and geophysics. You will become part of an academic team working to address major challenges of geotechnical infrastructure, including performance prediction
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and the core values that are critical for the long-term strategic growth of our division and the university. For more information, please visit https://finance.rutgers.edu/ . Posting Summary Rutgers
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for Predictive Product Properties (MTV)". Your research focuses on the experimental and material-modelling foundations required to enable predictive and controlled TVAM. You will be embedded in the Processing
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applications (https://www.cbr.washington.edu/analysis) to perform statistical analyses relevant to fish, dam, water, and natural resource management. The Software Engineer will play a lead role in the
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apply AI and data-driven modelling to predict system efficiency - balancing air purification with energy consumption. It will also explore how sensor feedback can control treatment systems and communicate
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predictive models for evaluation of the role of dietary in health and disease and establish personalized dietary strategies for more effective disease prevention. In many cases, the work involves time series
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
. Scientific Environment The Nonlinear Systems and Control group (https://www.aalto.fi/en/department-of-electrical-engineering-and-automation/nonlinear-systems-and-control ) in the School of Electrical