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Field
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institutions. Mentoring students (at a MSc and PhD levels), helping them with their research projects, and advising on methodology and academic writing. Organize dissemination and communication activities within
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-based hail climatology for France, Switzerland and Northern Italy, including hail swaths per event across more than a decade. Design, develop and train geostationary satellite-based hail algorithms using
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multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and their dynamics. Conducting literature searches, manuscript preparation, and
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: Research: Development and validation of predictive maintenance algorithms for solar farms. Interface with industry partners for knowledge sharing and feedback. Play a key role in reporting to the funding
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requirements, as well as the design of data models, synchronisation algorithms and analysis and learning models applied to brain and physiological signals. The objectives of this fellowship are: 1. To survey
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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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Responsibilities: Undertake research on algorithms and data systems for next-generation data preparation and data cleaning for data analytics. Produce research papers, reports, and presentations as required by
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architectures for explainable dual-process computation Design and development of deep neural network architectures and algorithms for the implementation of dual process computation approaches that improve
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algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating interpretable insights through novel analytics and
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of classical and hybrid classical-quantum algorithms for treating the correlations. This position offers exciting opportunities for collaboration within UQ, across the QDA network, and with external research