197 machine-learning-"https:"-"https:"-"https:"-"Linnaeus-University" positions at Zintellect
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areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
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. Description: Theoretical research and computer simulation are carried out with emphasis on observations of space plasmas. Specific interest areas include (1) nonlinear phenomena in unstable collisionless
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, austere conditions. Learning about military deployment health and gain experience in environmental data collection. Contributing to solutions for difficult environmental health problems in complex
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measurements, airborne measurements from field campaigns (e.g., NASA CAMP2EX, ARCSIX) or surface observations. Research involving machine learning techniques will be strongly encouraged. Field of Science: Earth
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candidates who would like to learn and gain experience in how to plan and perform large animal research and/or burn research in a military medical environment. What will I be doing? As an ORISE participant
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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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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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high school seniors, who are pursuing undergraduate studies in STEM, with an opportunity to explore the world of agricultural science through hands-on learning experiences. Participants will have the