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National Laboratory (ORNL). The NEEM Group focuses on pushing the limitations of materials, sensors, and experiments through technology development, advanced instrumentation, irradiation experiments, and
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sensing, IoT sensors, and climate models. Design and implement deep learning models for forecasting extreme weather events such as floods, droughts, and heatwaves, integrating probabilistic approaches
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Job Description Job Responsibilities: -Design and implement AI/ML models to analyze large-scale agricultural datasets (e.g., field trials, satellite imagery, IoT sensor data). -Develop pipelines
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sensor data, with applications in disease modeling and the development of material science-based innovations. These efforts aim to optimize system performance and uncover novel biological insights in close
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-criteria, defining their formalization as fuzzy subsets, and characterizing their uncertainty; Integrating Machine Learning algorithms to better account for low-level sensor data (precipitation, wind-driven
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Responsibilities: Digital Tool Development:Develop digital tools to monitor and analyze the determinants of urban health using sensor data, public databases, and GIS. Predictive Modeling:Design predictive models
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of Oxford. The post is funded by the National Institute for Health and Care Research (NIHR) and is fixed term for 24 months. The researcher will develop multi-sensor 3D reconstruction algorithms to fuse
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fabrication of bioelectrical platforms with clinical translation as the ultimate goal. Using unique bioelectrical (physical and chemical) sensors and actuators will allow us to tackle grand biomedical
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quantum networks and quantum sensors. Point defects in wide-bandgap solids are an example, where the deterministic interaction between emitted photons and electronic and nuclear spins enables photon
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analysis for more geometries and with a reduced number of sensors - Implementation of the MSE method on a cylindrical structure immersed in water and sensitivity analysis - Algorithmic and experimental