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, cognition, and multimodal imaging studies of Parkinson's disease and is directed by Drs. Nicolaas Bohnen, MD, PhD (Director) and Prabesh Kanel, PhD (Co-director). We are looking for a post-doctoral candidate
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include, but not limited to, computational approaches such as AI and machine learning; methodological foundations and computational approaches for AI for biomedicine, Bayesian inference, cancer imaging
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. Responsibilities* Lead and contribute to innovative research studies focused on the central mechanisms of chronic pain. Design, analyze, and publish research using multimodal imaging (PET, MRI, fNIRS, EEG) and GenAI
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relevant expertise in cancer biology, cell signaling, or molecular imaging. Contact information for three references should also be provided with your application materials.? Job Summary The Humphries Lab
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, architectural history, cross-regional and cross-cultural practices, ecocriticism, theories of the image, and visually-informed theoretical and other frameworks, are welcome to apply. Mission Statement The mission
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* The successful candidate will work on projects performing analysis of PET neuroimage data, including kinetic modeling and multimodal data synthesis. Responsibilities will include image data processing and analysis
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on developing new stable organic radical polarizing agents for nuclear spin hyperpolarization in NMR spectroscopy and imaging applications. Project Overview: This research aims to leverage radical dynamics
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a postdoctoral fellow to provide primary analysis of imaging and other data related to the Udall Center's primary research study and a similar study, the University of Groningen Dutch Parkinson Cohort
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approaches - including molecular genetics, RNA sequencing, FACS, biochemistry, confocal imaging, and behavioral analyses - along with integrative -omics workflows. Applicants must be able to apply
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health records (EHR), waveforms from bedside monitors, radiology images and wearable sensors. This position offers a unique opportunity to work closely with clinicians on applications of machine learning