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This research project, “Sensor-Free Machine Learning for Physiological Correction of Brain Functional Imaging: A Lifespan Pilot Study,” investigates how machine learning can be used to improve
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Senior Machine Learning Engineer (Full‑Stack / Applied‑Research) – Mission Innovation Lab At the SEI AI Division, we conduct research in applied artificial intelligence and the engineering questions
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 14 hours ago
image processing, disease detection, diagnosis, and therapeutic monitoring. The program addresses critical regulatory challenges posed by AI devices that can continuously learn and adapt, including
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. The aim of the thesis is to fit a machine learning (ML)-based kinetic model of methanol synthesis to an extensive experimental data set. The necessary balance equations and a detailed physical model are
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challenges facing the nation. We are seeking a full-time Senior Artificial Intelligence and Machine Learning Research Scientist who will support the Cyber Resilience and Intelligence Division (CRID) in
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Apply and key information This project is funded by: Department for the Economy (DfE) Summary CNC machining delivers high precision but is costly, rigid, and limited in adaptability. Robotic
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RAP opportunity at National Institute of Standards and Technology NIST Incorporating Theory and Domain Knowledge into the Machine Learning of Polymeric Systems Location Material Measurement
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Requisition Id 15751 Overview: The Advanced Computing in Health Sciences (ACH) section at the Oak Ridge National Laboratory is seeking qualified applicants for a Machine Learning Engineer position
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PhD at the Forefront of Computational Solid Mechanics and Machine Learning School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Dr J L Curiel Sosa Application
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable