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. Learning Objectives: Under the guidance of a mentor, this training opportunity may include: Gain focused training in federal nutrition, physical activity, and obesity surveillance; related research methods
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Mexico (UNM) invites applications for one or more Postdoctoral/Research Scientist positions to contribute to a DOE-funded GENESIS research program applying artificial intelligence and machine learning
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well as evaluate the biological activity of these compounds. Learning Objectives: The participant will have the opportunity to learn and use basic aseptic techniques needed for surgical procedures and long-term cell
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. Programming experience in Python, Matlab, and SQL. Mathematical modeling and data analysis. Machine/Deep learning (e.g. Pytorch, TensorFlow, JAX). City of Hope employees pay is based on the following criteria
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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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will also gain experience analyzing and visualizing trends related to importing infectious biological agents into the United States. Learning Objectives: Gain experience supporting CDC efforts to prevent
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or relocate to Atlanta, GA. Research Project: This opportunity focuses on implementation, evaluation, and organizational learning activities related to the Centers for Disease Control and Prevention (CDC) Rural
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have an interest in substance use, posttraumatic and/or operational stress, suicidality, and artificial intelligence/machine learning (AI/ML). What will I be doing and why should I apply? As the selected
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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
planta via genetic engineering. Learning Objectives: Under the guidance of a mentor, you will: Learn about the development and application of natural products for sustainable agricultural pest management
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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian