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You must apply at: https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0164
Under the guidance of a mentor, the fellow will have the opportunity to gain experience in and learn about the challenges of investigating dietary patterns and human health to develop new methodological machine learning approaches. The fellow will be housed in the Food Components and Health Lab at the Beltsville, MD Human Nutrition Research Center, but will also work closely with the Food Surveys Research Group and Methods and Applications of Food Composition Lab. These three units consist of food chemists, nutritionists, and physiologists with extensive expertise in assessing dietary patterns, dietary assessment, food intake, food composition, public health, and human health outcomes. Our Center has rich dietary datasets collected using methods which provide a daily detailed snapshot of dietary intake and behavioral patterns, which include details at the food level and contextual information about eating events. We also have measured markers of food intake and dietary patterns from urine, blood, and feces of research participants within these datasets which can be used for multiple -omics applications for markers of food intake and metabolism, including microbiome, metabolomics, and genomics. The high dimensionality and complexity of all this information combined outpaces standard statistical applications, thus are ripe for Artificial Intelligence (AI) and Machine Learning (ML) techniques to advance the understanding of how dietary patterns influence different aspects of human health.
Beltsville, Maryland, United States