259 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Zintellect in United States
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medical biological defense research. USAMRIID’s mission is to provide leading edge medical capabilities to deter and defend against current and emerging biological agents that threaten both the military and
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instrumentation and analysis, data management, software applications, record keeping, compliance training, and the principles of scientific study design. Learning both general and specialized research skills
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Germplasm System - https://www.ars-grin.gov/Collections#plant-germplasm . Our mission is to conserve, document, distribute, characterize, and evaluate crop germplasm for crop improvement research. Our
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organization. For more information about the 711th Human Performance Wing, please visit https://www.wpafb.af.mil/afrl/711hpw/ . About ORISE This program, administered by Oak Ridge Associated Universities (ORAU
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necessary to become credentialed as a Principal Investigator Applying a broad range of statistical and machine learning methods to human performance data collected in real-world settings Developing
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and weaknesses for end-users. Help develop new or improve existing soil moisture estimates using NISAR and other datasets utilizing artificial intelligence (AI) and machine learning. The outcome from
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& Amputation Center of Excellence (EACE) is a unique organization within the Department of War (DoW) consisting of teams of researchers embedded at the point of care within multiple Military Treatment Facilities
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Organization DEVCOM Army Research Laboratory Reference Code ARL-C-CISD-300144 Description About the Research Current approaches optimize machine learning training largely by exploiting Deep Neural
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collection of streaming sensor data. This project focuses on utilizing state-of-the-art reinforcement algorithms to 1) dynamically learn from multi-agent actions and context, 2) evaluate the environment and
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learn how phenotypic datasets are integrated with genomic data for association analyses, genomic selection, and AI-driven methods, including machine learning and deep learning, to enhance germplasm