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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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on a computer Occasional standing and walking within the office Ability to lift up to 20 lbs (e.g., office supplies, files) Manual dexterity for keyboarding and handling paperwork Clear vision (with
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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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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
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years of relevant experience preferred. Computer proficiency on MAC-based platforms Proficiency in Adobe Creative Suite - Photoshop, Illustrator, InDesign and other layout programs Knowledge of design
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-time, hourly Analyst for a role that involves a large breast cancer dataset. The role includes data cleaning for a large, medical dataset of MRIs and machine learning analysis of time-series data