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of the MAE Student Machine Shop located in Scott Laboratory. This position reports to the MAE Shops Manager and plays a key role in supporting student learning, hands-on engineering education, and departmental
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identification in greenhouse environments. Apply machine learning to analyze plant and environmental data. Support the integration of AI algorithms with automated sensing systems for real-world deployment. Assist
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implement computer vision pipelines for crop monitoring, plant stress detection, and disease identification in greenhouse environments. Apply machine learning and deep learning models (semantic segmentation
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identification in greenhouse environments. Apply machine learning to analyze plant and environmental data. Support the integration of AI algorithms with automated sensing systems for real-world deployment. Assist
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that may be viewed while carrying out various functions of the position. Position Summary The Student Intern is responsible for assisting the learning and development consultants focused on extended reality
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Interaction or related fields. 4-8 years of relevant experience preferred. 4-10 years of professional experience in instructional/learning design or related field desired. Experience designing and implementing
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machine learning. In addition to leading research initiatives, the postdoc is expected to collaborate closely with graduate students, faculty, and industry and DoD partners. Key responsibilities include
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machine learning. In addition to leading research initiatives, the postdoc is expected to collaborate closely with graduate students, faculty, and industry and DoD partners. Key responsibilities include
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infrastructure and customer service. The analyst supports the oversight of the Research Commons computer lab, explores hardware and software solutions, and drives the adoption of new and emerging technologies
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handling. Major duties include: Design and implement computer vision pipelines for crop monitoring, plant stress detection, and disease identification in greenhouse environments. Apply machine learning and