161 machine-learning-"https:"-"https:"-"https:"-"https:"-"Iscte-IUL" positions at Zintellect
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tools and machine learning for advanced image analysis, weed-crop detection, and mapping. Experience in data collection, processing, and interpretation. Strong background in precision agriculture and
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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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to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including artificial intelligence (AI) and machine learning, to help
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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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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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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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in each crop area and learn basic agronomic, data collection, and plant breeding methodologies in trials and nurseries planted at the USDA-ARS. Learning Objectives: The project assignments will provide
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. market access. The approach will include metagenomics and bioinformatics to understand genetic diversity of the pathogen. Learning Objectives: During this project, the participant will be involved in
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of ARS National Programs 305 (Crop Production) and 304 (Crop Protection & Quarantine). The successful candidate will learn about project management by being a part of research aimed at identifying
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of genes and proteins as regulators of physiological or immunological traits. Learning Objectives: Under the guidance of the mentor, the candidate will gain experience in and learn to utilize a functional