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for decision making. [1] Sun et al., "Mind your weight(s): A large-scale study on insufficient machine learning model protection in mobile apps.", USENIX Security, 2021.
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of statistical signal processing, inference, machine learning and dynamical systems theory to develop new semi-analtyical filtering approaches for state and parameter estimation to infer neurophysiological
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: Developing and deploying machine learning models (e.g. graph neural networks, neural force fields, diffusion models) for molecular property prediction and molecular generation. Integrating quantum chemistry
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the area of end-to-end modular autonomous driving using computer vison and deep learning methods. This includes developing an efficient and interpretable image processing, vision-based perception and
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Build and test predictive models using machine learning techniques Drive methodological innovation in neurophysiological data analysis Contribute to publications, grant submissions and independent
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significant step forward in machine translation capabilities. However, "NMT systems have a steeper learning curve with respect to the amount of training data, resulting in worse quality in low-resource settings
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of innovative computational procedures and methodologies, addressing a national skills shortage and enabling timely progress on a high-impact research initiative in modern econometric modelling. The role provides
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institutional policies. To be considered for this role, you must hold a doctoral qualification in operations research, operations management, business analytics, data science, machine learning, or a closely
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. Application of artificial intelligence/machine learning to the big data from genetics and omics is well recognized in healthcare, however, its application to the data reported everyday as part of the clinical
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CRISPR data; (6) provide recommendations on precise drug dosages based on genomic and clinical profiles. The project will build machine learning and/or deep learning sub-models and concatenate them to form