232 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions at Zintellect
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the devastating disease avian coccidiosis. The secondary goal is to compare various Eimeria spp. to identify genes involved in intestinal cell specificity, virulence, and markers of drug resistance. Learning
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to develop novel statistical techniques, analyze satellite and other remote sensing data, implement machine learning algorithms, assess numerical model performance, improve risk assessment tools, and deepen
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spectroradiometers. Ability to apply AI tools and machine learning for advanced image analysis, weed-crop detection, and mapping. Experience in data collection, processing, and interpretation. Strong background in
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tolerance for varietal selection. Learning Objectives: Participant will gain laboratory, field, and programming skills to develop the digital twin and other AI models using ground and above-ground sensors and
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of the opportunity involve various outdoor conditions requiring moderate exertion and traversing the landscape of the MEF. Additionally, the fellow will experientially learn about and participate in the Forest Service
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pathway for undergraduate students. EQuIPT is a 10-week, full-time, student-focused internship. Under the guidance of a mentor, you will learn and gain experience engaging with LQC researchers, industry
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experience with time-series data analysis and machine learning including reinforcement learning. Applicants should be proficient in Matlab and/or Python Point of Contact ARL-RAP Eligibility Requirements
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, Environmental Sanitation and Hygiene, and Laboratory Services. What will I be doing? Under the guidance of an epidemiologist mentor, you will be involved with and learn how to: Collect, evaluate and provide
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the use of workflow tools, development environments, and resources to contribute to and implement shared bioinformatic workflows. Experiences may extend into training on Machine Learning and AI models as
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the discharges of wastes associated with conventional sorbent synthesis; • Learning on applied, cutting-edge projects with global impact while being mentored by the nation’s leading energy scientists