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a doctoral degree must obtain the degree before starting the appointment. Preferred Skills: Practical experience setting up and maintaining geophysical, soil and/or agronomic sensor networks. Ability
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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collecting soil, plant, and water samples; monitoring soil moisture with advanced sensors; organizing and processing research data; and contributing to modeling efforts that examine water and nutrient dynamics
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variety of meteorological and snowpack sensors. Learn to document avalanche activity and environmental conditions using standardized protocols. Data Analysis and Modeling Train to process and analyze
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to classify rangeland plant species, and (2) using transfer learning to adapt deep learning models for imagery analysis to varying UAV sensors and conditions. These techniques will allow you to identify and
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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
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when these arise. Research Project: The Respiratory Virus Hospitalization Surveillance Network (RESP-NET) Team, within the Surveillance, Epidemiology and Prevention Branch of CORVD, is seeking a
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well as communicate with research networks within the scientific community. Learning Objectives: As part of this learning experience, you may: Learn how grapevine populations and germplasm are evaluated to identify
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semiconductor-free emerging sensing materials (plasmonic metal metasurfaces). You will consider how this might represent the first steps to a new generation of inexpensive and uncooled sensors, whether
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training, travel to communicate findings, and professional networking will also be available. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn to: (a) plan, execute