-
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
-
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
-
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
-
. Additionally, we intend to measure root water uptake using sap flow meters. The data will be integrated using recently developed physics-informed neural networks in order to translate apparent resistivity data
-
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
-
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
-
. Research learning activities may include: Designing and implementing Discrete Event Simulation (DES) models to simulate complex system workflows, queuing networks, logistics, and operational state changes
-
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
-
networks. Build a publication record and strengthen research skills through engagement with academia, federal science, and operational avalanche forecasting communities. Mentor: The mentor