129 machine-learning "https:" "https:" "https:" "https:" "https:" "Dana Farber Cancer Institute" research jobs at Zintellect
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of traumatic extremity injuries and amputations with a specific focus on translating their findings into clinical practice to improve the care of injured Service Members and Veterans. To learn more, visit: https
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research in several areas. Learning activities will focus on: The development and characterization of animal models and/or microphysiological systems for viral agents. Emphasis is placed on determining
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degradation that could occur to C-130 crew members from extended exposure to environmental insults. This research will also inform the development of effective human-machine systems and healthy Airmen protocol
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, software applications, record keeping, compliance training, and the principles of scientific study design. Learning both general and specialized research skills that will support advancing your scientific
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contributions are valued and your growth is encouraged. This fellowship is ideal for enthusiastic, team-oriented individuals eager to learn and make meaningful contributions to the field of emerging infectious
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. https://www.ars.usda.gov/pacific-west-area/wapato-wa/temperate-tree-fruit-and-vegetable-research/ Research Project: The selected participant will engage in cutting-edge research focusing on molecular
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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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to cell and gene therapy. Will learn to use advanced manufacturing tools and strategies to gain a deeper understanding of challenges associated with T cell-based immunotherapies (such as CAR-T cells). Will
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. Develop skills in coupling crop and hydrology models at watershed scales. Gain experience validating models using large, multi-source datasets. Learn to apply high-performance computing and machine learning
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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