259 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" positions at Zintellect
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of laboratory mentors. Activities will include computer programming related to database development, extension of the IDS graphical user interface, and integration of our crop and soil models. Database activities
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statistical analyses using a range of software tools. Learning Objectives: They will learn how plants and soils respond to genetic, environmental, and management inputs, and why understanding these interactions
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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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, statistics, and field-lab approaches. Learning Objectives: The participant will receive training in plant molecular biology, genetics, and genomics. This research is expected to result in increased learning
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Sensing Nuclear Science and Weapon Effects Artificial Intelligence, Machine Learning, and Cyber Security Materials, Extreme Environments, and Optical Sciences Remote Sensing and Radiation Detection
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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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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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seeds. This research will help to unravel key indicators of biological relevance during seed quality testing procedures and contribute to a healthy national and international seed trade economy. Learning
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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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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