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collaborating in interdisciplinary R&D environments, translating research concepts into deployable prototypes or field-ready systems. Practical experience working with camera systems and computer vision pipelines
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California State University, San Bernardino | San Bernardino, California | United States | about 14 hours ago
calculations; work independently; make sound decisions and recommendations regarding payroll activities; use current computer word processing and spreadsheet software programs, web browsers, electronic
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research in the state-of-the-art areas in artificial intelligence, natural language processing, large language models, and computer vision; (2) secure external competitive research grants in various funding
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and productive manner. Knowledge of legal implications in clinical practice and documentation. Knowledge of performance improvement and evidence-based practice. Basic computer skills. Ability to assess
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to multitask and balance heavy workloads. Must have willingness, flexibility and adaptability to shift priorities in rapidly changing situations if needed. Knowledge of common computer application programs
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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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. The researcher will develop novel research that applies advanced data science, machine learning and deep learning to various different data modalities. An ambition of this team is to implement predictive modelling
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. We encourage you to apply even if your experience doesn't match every listed requirement. #YouBelongHere To learn more about our background check program, please visit: https://hr.ucdavis.edu
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requirement. #YouBelongHere To learn more about our background check program, please visit: https://hr.ucdavis.edu/departments/recruitment/ucd/selection/background-checks Apply for Job
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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