109 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" Fellowship positions at Zintellect
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300 species. https://www.ars.usda.gov/northeast-area/geneva-ny/plant-genetic-resources-unit-pgru/docs/about-pgru/ Research Project: Participants will have the opportunity to explore genetic variation
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generated quickly and regularly. Help develop machine learning techniques for feral swine abundance in data sparse environments. Collaborate with APHIS Wildlife Services (WS) to integrate data and model
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to multidisciplinary research aimed at advancing military medicine. What will I be doing? This opportunity offers a hands-on learning experience within a collaborative research environment focused on combat casualty
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research applying artificial intelligence (AI) and machine learning (ML) techniques to analyze cervid movement patterns. GPS telemetry data obtained from free ranging cervids will be used by the participant
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manufacturing) extracellular vesicle manufacturing and characterization high content imaging biomaterials bioreactors multiomics (proteomics/metabolomics) immunology machine learning/AI single cell profiling
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, assessing enrollment patterns, and analyzing sex-specific treatment outcomes. Learning Objectives: Under the guidance of a mentor, you will: Gain foundation knowledge in antiretroviral drug classes, meta
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associated risks to U.S. farmlands and agricultural resources. Learning Objectives: During this appointment, the participant will develop hands-on experience working within an interdisciplinary research team
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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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decision-making by forest managers, planners, and policy makers. This project will inform the Forest Service’s Resources Planning Act (RPA) Assessment. Learning Objectives: The participant will have the
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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