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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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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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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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pathology, bioinformatics, comparative evolution and other areas. You will also have opportunities to attend scientific conferences for presenting the research results and establish collaborative networks
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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
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structures, molecular networks, and disease-resistance phenotypes. Artificial intelligence (AI), machine learning, and bioinformatics will connect genotypes with phenotypes and identify maize and fungal genes
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influence outcomes based on early immune response dynamics. Opportunities for additional training, communication of findings, and professional networking will also be available. Mentor(s): The mentor