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, EEG/MEG, behavioural methods, computational modelling, automated neuroanatomical phenotyping, machine learning and advanced statistical approaches. The doctoral project will be developed jointly
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expertise to investigate individual differences (that might predict learning and outcomes), underlying cognitive and neurobiological mechanisms, and intervention outcomes using tools including, but not
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Mathjobs.org | 24 days ago
machine learning methodologies to address them. The search is broad and welcomes research spanning modern statistical methods, machine learning, AI, and emerging approaches to data science, with
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machine learning methodologies to address them. The search is broad and welcomes research spanning modern statistical methods, machine learning, AI, and emerging approaches to data science, with
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Cognitive Neuroscience Apply for this job See advertisement About the position Position as PhD Research Fellow in Machine Learning and Applied Cognitive Neuroscience available at the Department of
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not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and functional
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, automated neuroanatomical phenotyping, machine learning and advanced statistical approaches. The doctoral project will be developed jointly with the successful candidate and tailored to their
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simultaneously from both partners. Machine-learning algorithms will subsequently be developed, trained and tested to identify markers of reduced brain resilience during ageing. The project adopts an
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practices Course Description ARI 410 - Machine Learning CSC 375. (4) Study of machine learning, a subfield of artificial intelligence intersecting with statistics, cognitive science, information
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related fields. Prior lab experience is preferred but not required. Candidates with research experience in machine learning and artificial neural networks, are strongly encouraged to apply. The candidate