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part-time post (0.2FTE) ideal for someone working in industry or with industry experience. This is because we want to bring in expertise with data processing and machine learning pipelines, and their
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and analyze data for presentation; work at the command-line in for neuroimaging processing and analysis (FSL, AFNI, in-house software tools). Assist with modifying computer scripts and analytical
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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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HCI and cybersecurity, to cancer research tools and methods for numerical analysis and machine learning. The research work takes place in a multidisciplinary team with a focus on image processing with
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) Demonstrable experience/training in the concurrent use of fMRI and EEG Demonstrable experience/training on the use of phenomenological measures Experience with Machine Learning methods to assess aspects
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skills. Aware of the ethical issues around working with Big Data. Desirable criteria Experience applying advanced statistical or machine learning methods to complex datasets. Evidence of involvement in
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from Ulster’s wide-ranging expertise in Computational Neuroscience, Neurotechnology, and Machine Learning and access to cutting-edge neuroimaging facilities such as NIRS/EEG/MEG and will interact with
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opportunity to advance the integration of machine learning with multimodal biological data (including genomics, neuroimaging, digital phenotyping, and clinical information) to address foundational questions in
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development in large multi-site cohorts; and 3) developing advanced machine learning models of task-related brain function. The fellow will have the opportunity to contribute to these projects and will also be
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syndrome. Targeted projects currently include the following: Use AI/machine learning approaches to develop a means to quantify and classify tic movements and vocalisations in Tourette syndrome/tic disorder