344 machine-learning-"https:"-"https:"-"https:"-"https:" Fellowship positions in United States
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using both conventional breeding methods and marker assisted breeding. Learning Objectives: The participant will partner with the mentor to learn more about: Conducting phenotype trials related to maize
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across the dairy supply chain. The participant will also learn about statistical analysis of data in consultation with ARS colleagues. Learning Objectives: Under the guidance of a mentor, the participant
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consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service
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sampling of the parameter space of eclipsing binary observables, most notably photometric data from NASA’s Kepler and TESS missions. In parallel, the applicant will be given an opportunity to teach
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We highly recommend reviewing our department website’s Faculty pages to learn more about our faculty, their labs, and their research interest before applying. Please apply through the ARIeS portal
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analysis and collaboratively advance the development of standard operating procedures for advanced microscopy. Learning Objectives: Through this educational opportunity, the selected candidate will develop
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. Learning Objectives: Develop skills in plant breeding including hybridization techniques. Become familiar with ploidy manipulation techniques and analysis using flow cytometry, microscopy, and conventional
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commercialization of corn hybrids with resistance to aflatoxin contamination and insect damage. Learning Objectives: This mentorship opportunity offers research training in applied plant breeding techniques as
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(e.g. embryology, and medical imaging) and research methodology (e.g. biostatistics and scientific writing). The research assistant will have the opportunity to teach in the anatomy curriculum and to
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(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health