126 machine-learning-"https:"-"https:"-"https:"-"https:" Fellowship positions at Zintellect
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the soil-water-plant-air continuum using process-based models. You will learn how to take proper soil, plant and air samples that influence carbon and nitrogen dynamics and learns how soil and plant
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against expensive and dangerous health threats, and responds when these arise. Research Project: ATSDR Region 6 seeks to host an ORISE Fellow in Dallas, Texas. The ORISE fellow's primary learning experience
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the application of machine learning/artificial intelligence (ML/AI) in environmental health. This project aligns with ATSDR's current strategic initiatives and will provide you with opportunities
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of multiple surveillance and administrative data sources. Development of reproducible analytical workflows using programming languages such as R and Python. Application of machine learning and predictive
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. This fellowship requires in-person participation in Manhattan, Kansas. Learning objectives: During this appointment, you will have the opportunity to: Gain experience in sorting and identifying insects of medical
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Response is seeking a fellowship participant to receive hands-on training in the coordination, development, and implementation of advanced software systems. Through mentorship and experiential learning, you
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managing diabetes using computer simulation models. Learning Objectives: You will learn: How to synthesize and translate empirical evidence on cost-effectiveness of interventions for the prevention and
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machine learning methods, including cluster analysis and predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications
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activities, learning how to organize and synthesize information from diverse scientific programs. Under the guidance of a mentor you will perform a structured gap analysis to identify unmet research needs and
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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