202 machine-learning-"https:"-"https:"-"https:" positions at Zintellect in United States
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, microbiology, infectious diseases, animal agriculture, or food safety. Experience in applied statistics, data science, machine learning, mathematical modeling, epidemiology, disease ecology, and PCR assay
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• Unmanned Aerial System development, testing, and research • Biomechanics research in musculoskeletal and gastrointestinal systems • Machine learning • Mechatronics and robotic systems • Wind Tunnel
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helping the office in research, economic, and science security to improve the impacts of USDA agricultural science and innovation. Under the mentorship of a Senior Advisor, the fellow will learn about
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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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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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collection will include automated phenotyping measurements, as well as crop development, phenology, photosynthesis, and yield. Learning Objectives: During the appointment, you will; Gaining experience with
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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