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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning
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the supervision of the PI, including proposal development and preparation of high-quality publications in top computer security, privacy, embedded systems, sensing, and networking venues. Pursue research topics
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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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quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a welcoming and international work environment. The research group in
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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a strong
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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simulation methods and quantum theoretical calculations in principle can address this but have hitherto struggled with tackling such challenging systems. With the emergence of machine learning methods in
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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Movement Sciences at Rutgers University is seeking a highly motivated Post Doctoral Associate to work on translational projects at the intersection of biomechanics, machine learning, exergaming, and mobile
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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model