106 machine-learning "https:" "https:" "https:" "UCL" "UCL" "UCL" Fellowship research jobs at Zintellect
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. Research Project: Joining a team of landscape and fire ecologists to learn about and support geospatial analysis of fire mitigation implementation in the highly fire prone lands of Kona and Kohala on Hawaii
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. Through these experiences, it is anticipated that you will learn how to: Operate and develop custom aerosol generation equipment including software modifications for associated chambers. Operate
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attenuated centrin deleted Leishmania major parasites (LmCen-/-) using transcriptomic and metabolomic approaches. Learning Objectives: The opportunity to contribute on this project will allow you to acquire
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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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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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expected to learn both independently and collaboratively within a multidisciplinary research team, contribute to experimental design and data analysis, publish findings in peer-reviewed journals, and
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to exploit in their projects. Characterization of the alfalfa collection can aid in effective management of this important resource while improving and promoting its use by stakeholders. Learning Objectives
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conditions. Anticipated outcomes of the project include criteria for selection of fungal genotypes that will be developed into new biocontrol products for mitigation of crop aflatoxin contamination. Learning
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into HCV diversity and help guide the development approaches to test new HCV vaccines. Learning Objectives: Under the guidance of a mentor, you will learn to apply molecular biology techniques to generate
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students and collaborate on aquatic ecology field projects in southeast Alaska wilderness watersheds. Learning Objectives: Learn about bioenergetic food web models to quantify food web energy fluxes between