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problem-solving skills. Willing to attend and present at in-house and industrial meetings. Proven research experience in quantum machine learning, machine learning, and wireless communications (underwater
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DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close
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None Additional Preferred Experience working in one or more of the following areas: Longitudinal data analysis Predictive modeling/machine learning models Biostatistics / epidemiological modeling
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adapters should be created and tested for previously selected detection methods, which can reliably bypass these detection methods. Be part of change Researching and implementing novel machine learning
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Description The Fuqua School of Business at Duke University invites applications for the position of Adjunct Professor to teach the course “Foundations of Capital Markets” in Fuqua’s MMS program in the fall
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established in the areas of electronic and electromagnetic simulation and design, machine learning and artificial intelligence in electrical engineering, electrical low-frequency and high-frequency measurement
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to machine learning and AI projects for satellite systems. We are looking for a candidate capable of developing ML models and optimization algorithms specifically designed for highly dynamic satellite
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accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models, including large
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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
and features for the pre-symptomatic detection of changes in the physiological status of plants, developing, training, validating, and comparing predictive machine learning and deep learning models
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advanced analytical and statistical techniques to extract actionable insights from complex datasets. Train, evaluate, and continuously refine deep learning and machine learning models, prioritising