-
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
-
spray chambers and LI-COR gas-exchange systems, as well as opportunities for managing field, growth-chamber, and greenhouse studies to investigate weed control in cropping systems. Learning Objectives
-
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
-
on wildlife disease impacts in ungulates around the Greater Yellowstone Ecosystem. Learning Objectives: Through this mentored research experience, you will expand your knowledge of wildlife disease ecology
-
genetic tools; analyzing and summarizing research data for internal and external reporting; and assisting in the preparation of manuscripts for submission to peer-reviewed journals. Learning Objectives
-
-performance computing (HPC). The objective of these fellowships is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each applicant and amenable
-
-performance computing (HPC). The objective of these fellowships is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each applicant and amenable
-
architecture trait identification. The overall objectives of the project include: Deep understanding of plant water relations to extreme environmental stresses. Hands-on experience in measuring leaf gas exchange
-
validating the gene functions with transformation and molecular experiments. Lastly, you will assist in analyzing the gene-edited plants to improve grain quality in barley and oat. Learning Objectives: You
-
, integration, and analysis of large, diverse datasets that benefit from high-performance computing (HPC). The objective of these fellowships is to facilitate cross-disciplinary, cross-location research through