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connectivity and graph-theoretic analyses Familiarity with MR sequence programming (Siemens or GE platforms) Machine learning / AI applied to neuroimaging data EEG acquisition and analysis Use of neuroanatomical
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
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disorder. This project investigates early neural markers of psychosis by integrating multimodal neuroimaging with genetic and transcriptomic data and applying machine-learning approaches to identify
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or similar) Preferred Qualifications: Experience with MRI/fMRI/DTI, PET, multimodal fusion, and/or machine learning Strong programming skills (Python/MATLAB), version control, and HPC workflows Special
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engineering, psychology, computational neuroscience, or a related discipline; Strong background in human neuroimaging analysis (e.g., FSL, SPM, AFNI, MRtrix, or equivalent); Demonstrated experience with
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, and R. Experience with neuroimaging tools (SPM, AFNI, FSL), clinically-acquired neuroimaging, machine learning, and working with Epic databases (Clarity, databricks, Caboodle) is strongly preferred but
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routine background checks. Essential Duties and Responsibilities Neuroimaging data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms
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knowledge of programming, including Linux, Python, and R. Candidates having background knowledge in neuroimaging, machine learning, and/or genomics/genetics are encouraged. Excellent communication and writing
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College of Health Sciences, College of Arts, Music, and Design, and the College of Engineering. It brings together a unique combination of disciplines, including neuroimaging, cognitive neuroscience
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health, and artificial intelligence; investigate AI applications for personalized music therapy and brain health interventions; and explore machine learning approaches to understanding musical cognition