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Field
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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of the postdoctoral researcher will include: To work closely and proactively with Prof Anton van den Hengel to scope and develop research ideas. To develop algorithms, machine learning models, Python modules
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develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung infection. As part of this work, the postholder will
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optimiser that accelerates both workflow efficiency and materials discovery. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate surrogates
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ideas. To develop algorithms, machine learning models, Python modules, demonstrators and training pipelines for publication and translation into commercial products that can be widely and reliably adopted
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include: Skills in mathematical modelling and machine learning of relevant physical glacier processes (ice sheet and mountain glaciers), with proficiency in MATLAB/Python/Fortran, and related software tools
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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, creates tools for synthetic observations, accelerates forward modeling using machine learning, and couples physics-based models with simulation-based inference techniques. Our goal: create a robust
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machine learning methods. The successful candidate will lead an independent research project dedicated to identifying abnormal behavior and neuronal activities in circuits of murine models of 22q11.2 and