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Field
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neuroscience, neuroimaging, machine learning and neurofeedback, providing an interdisciplinary research environment for candidates interested in the neural basis of emotion and consciousness. Job description: We
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. The ideal candidate will possess not only a deep conceptual understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. We
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or AI/machine learning/NeuroAI. For candidates with a neuroimaging background, this may include experience with data acquisition, preprocessing, and/or analysis; experience with fMRI is preferred, but
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computational neuroimaging initiative focused on individual-level brain characterization. It combines large-scale imaging data, normative modelling, harmonization and machine learning. The platform generates
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multivariate, statistical-genetic or machine-learning approaches. · Research involving developmental, ageing or neuropsychiatric cohorts, including longitudinal or large-scale population datasets. · High
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assigned Requirements A Ph.D. degree in Neuroimaging, Biomedical Engineering, Electrical Engineering, Physics, Computer Science, or other related disciplines, with experience or keen interest in processing
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Python and with machine learning toolkits; experience with software development practices, including git-based version control and CI/CD; excellent project management, analytical, problem-solving
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engine of adaptive development. Infants are conceptualised as active agents who shape their own learning by sampling information, reducing uncertainty, and updating predictions. We are building
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programming (e.g. Python, MATLAB or R) Experience applying advanced analytical techniques, including machine learning, to complex datasets Experience with neuroimaging analysis (MRI/fMRI) is desirable Strong
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coworkers in the methodologies. Required Qualifications: • Master’s Degree in Biomedical Engineering/Computer Science/Neuroimaging • Strong understanding of machine learning, deep learning for biomedical