343 machine-learning-"https:"-"https:"-"https:" Fellowship positions in United States
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Field
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microscopy systems that integrate machine learning, robotic control, and real-time data analysis to achieve autonomous imaging and interpretation of complex materials systems. The Fellow will design and
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traits in oats. Participate in data collection and genetic analysis of FHB resistance. Write manuscripts and present research at scientific conferences. Learning Objectives: The participant will receive
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rewarding component of the fellowship. Learning Objectives: As part of the fellowship, the selected candidate will gain hands-on experience and develop expertise in key areas of plant breeding and genomics
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mission is to help build America's airpower, by educating military and civilian Airmen to innovatively accomplish the Air Force's core missions, in support of joint operations, more effectively, efficiently
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regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status
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identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other characteristics protected by law. Pay
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selection. This research is conducted in a collaborative environment within the lab and larger unit, so the participant will learn how to perform both independently in the field and as part of larger teams
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to race, color, creed, religion, sex, sexual orientation, gender and/or gender identity or expression, marital or parental status, national origin, ethnicity, citizenship status, veteran or military status
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date to be determined. Basic Qualifications A PhD related to programming languages by the start date. Experience in machine learning and formal verification. Individuals with a demonstrated track record
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of incorporating sensors, spectroscopy, imaging, and machine learning techniques into the postharvest processing workflows and/or pre-harvest evaluation of food quality and safety. The participant will have the