138 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" research jobs at Zintellect
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assessment research with a focus on biosensor development. The research fellow will learn and apply techniques in biosensor fabrication and participate in testing and evaluating their performance
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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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. 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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. This project aims to establish a comprehensive surveillance platform that combines Illumina short-read and PacBio HiFi long-read sequencing with advanced machine learning approaches. The research will involve
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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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areas. This fellowship places a strong emphasis on the application of machine learning, artificial intelligence, and bioinformatics to solve complex biological problems. Potential research activities may
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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