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where new health hazards (such as vapor intrusion or changes in chemical toxicity) have emerged. Learning Objectives: You will have the opportunity to: Learn ATSDR’s approach to conducting public health
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& Amputation Center of Excellence (EACE) is a unique organization within the Department of War (DoW) consisting of teams of researchers embedded at the point of care within multiple Military Treatment Facilities
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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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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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translational vaccinology within a highly collaborative research network. Within this opportunity, you will be engaged in the following learning objectives: Applying machine-learning/AI platforms including
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incorporating artificial intelligence/machine learning functions in any of the previously listed training activities. Training is approved for remote appointments. Mentor(s): The mentor for this opportunity is
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are not currently captured by active systems. Learning Objectives: Learn about high-consequence bacterial pathogens and the role of national reference laboratories in public health surveillance and outbreak
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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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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