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Field
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Location SAINT LOUIS, MO 63110 Scheduled Hours 40 Position Summary The Fields lab utilizes advanced neuroimaging techniques combined with cognitive assessments, patient reported outcomes and
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, and data curation standards across local servers and high-performance computing (HPC) systems. Execute end-to-end neuroimaging data processing pipelines using Linux-based environments and specialized
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methodologies for quantitative neuroimaging and multiview data integration, as related to Alzheimer’s Disease and the impact of substance exposure on neurodevelopment. The ideal individual will have a strong
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. psychopharmacology, neuroimaging, psychosis) Desirable Criteria Training and/or experience with relevant psychiatric rating and/or neuroimaging measures Experience of acquisition and analysis of relevant neuroimaging
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the brain, to describe brain function comprehensively and integrate neuroimaging findings systematically across experiments, disorders, and species. The Group You will join Dr Andrea Luppi's research group
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(3.5 years) position at our Hawthorn campus Scholarship stipend of $42,440 per annum Are you passionate about epilepsy research, neuroimaging, and developing innovative data analysis pipelines
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) position at our Hawthorn campus Scholarship stipend of $42,440 per annum Are you passionate about epilepsy research, neuroimaging, and advanced data analysis? Join a cutting-edge research team at Swinburne
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, please visit the website: https://innovation.umn.edu/developmental-cognition-and-neuroimaging-lab/ Work Schedule: In-person, Work Hours varies, Work Days varies Duties/Responsibilities: Clinical Data
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campus HEW Level 6 salary + 17% super and staff benefits About the Role We are seeking a highly organised and technically capable Magnetoencephalography (MEG) Technician to join Swinburne Neuroimaging
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Università degli studi di Roma Tor Vergata Dipartimento di Biomedicina e Prevenzione | Italy | 10 days ago
supervisor’s guidance. Activities will include preprocessing and integrating available neuroimaging data and electrophysiological signals, learning shared representations, and comparison with dynamical models