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. Applying machine learning to New Zealand’s landslide inventories to model landslide location, character and dynamics. Integrating time-series and inventory data to develop new models to predict location
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greener transportation and energy. Building on recent advances, the successful candidate will use a powerful combination of dynamical systems theory, optimisation, DNS and machine learning to model and
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interaction, as well as participate in the full range of faculty responsibilities, including scholarly research and service to the University. Applicants should possess a PhD or master degree in
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data and machine learning processing pipelines. It will also focus on developing AI algorithms for semantically enriching data assets with domain knowledge, which feed into building knowledge graphs
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Experience with statistical analysis, machine learning methods, and high-performance computing Desirable: Experience with spatial transcriptomics, 3D genome analysis, or epitranscriptomics Familiarity with
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challenges from low carbon shipping and sustainable fuels to solar power technologies and advanced brain models. Learn more at https://mecheng.ucl.ac.uk . Within this dynamic environment, the Moazen Lab is
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, challenging project. Learning on the job isn't just a benefit – it's a must. Education, Qualifications and Experience Essential Criteria Applicants should hold a PhD in a relevant area of Engineering
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the expertise of the existing members of the School will be considered. Topics of interest include, but are not limited to: reasoning, autonomous systems, machine learning, AI and industrial processes, machine
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, Bayesian Statistics with a focus on nonparametric methods, Bayesian Computational Methods, Extreme Value Theory, Biostatistics, Probabilistic machine learning, Medical sciences and engineering applications
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain