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devices, and edge computing platforms. Deploy machine learning models to enhance process control and system responsiveness. Collaborate with industry partners to identify research challenges, co-develop
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Responsibilities of the Post Conduct research and development on sign language recognition using computer vision and machine learning techniques. Lead the implementation of inference models suitable for mobile and
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern thinking, connecting decades
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
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, molecular dynamics, and machine learning, to model battery electrolyte and solid electrolyte interphase (SEI), while collaborating with experimentalists. Qualifications • Ph.D. in Computational Materials
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Associate/Research Fellow with expertise in Artificial Intelligence (AI)/Machine Learning (ML), pedagogical research in Institutes of Higher Learning (IHLs), and web/mobile application development (both
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stimulating community. Fellows teach one small seminar during their fellowship year appropriate for graduate students and advanced undergraduates. Though courses are designed to fit the focal theme
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
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from date of hire. Preferred Qualifications Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond