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Knowledge of MLOps concepts, including dataset ops, training pipelines, evaluation frameworks, deployment and monitoring. Understanding of deep learning fundamentals and hands-on experience with model
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that preserves object identity or style. They should have a solid publication record in top-tier computer vision conferences such as CVPR, ICCV, or ECCV, and demonstrate proficiency in deep learning frameworks
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applications for one or more fixed-term, non-tenure-track full/part-time Instructor positions to teach on a term by term basis for the 2025-2026 academic year. Some of these appointments may be reviewed
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Faculty Salary Schedule: https://www.gavilan.edu/jobs/salary/docs/Faculty-Salary-24-25.pdf Assignment Start Date: Fall 2026 Semester About Gavilan: Located in the heart of California between San Jose and
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Research, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application
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, empowerment, and integrity to apply. Learn more about our progress towards Equity, Diversity, and Inclusion. https://staff.mq.edu.au/work/diversity-and-community If you're already part of the Macquarie Group
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Doctoral Candidate in computer vision and machine learning for developing novel deep learning method
Machine Learning (DM3L) Doctoral Candidate in computer vision and machine learning for developing novel deep learning methods for satellite-based tracking of global CO2 and NOX emissions of point sources 80
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with deep technical expertise and transferable skills to tackle tomorrow’s challenges. SIT collaborates with industry in our education, while benefitting them with our talent supply and collaborative
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VET requirements (or ability to learn quickly). Ability to work independently and as part of a team in a fast-paced environment. Salary details Appointment to this role will be at HEO 4 and will have a
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, with a focus on developing rigorous mathematical foundations for AI interpretability. Research directions include mean field theories of deep learning, data attribution methods, renormalization group