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disease variants, to gain insights into how cells adapt and work together to safeguard organ formation. Using advanced live-imaging techniques, the research team closely interrogates how developing tissues
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processing magnetic resonance imaging (MRI), such as arterial spin labeling and diffusion tractography using programming scripts, such as Python and C++ Self-driven, motivated, and creative in designing image
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graph neural networks for complex sensor networks such as those involved in brain imaging Develop and test data-driven methods for image and video processing for microendoscopy. Key Duties and
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uncover how epithelial cells organise in space and time under different physico-chemical environments to drive self-organisation processes, like condensates, that shape mesoscale structures enabling tissue
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to understand the role of subcortical circuitry in normal and pathological feeding and reward processes. Applicant must hold a PhD or equivalent degree in neuroscience, biochemistry, molecular biology, psychology
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magnetic resonance imaging using pulse sequence design, image reconstruction, and real-time image processing, for brain, body, and fetal applications. Responsibilities: The successful candidates will develop
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Position Summary The Integrated Biomedical Imaging Laboratory is seeking a Postdoctoral Research Associate interested in developing novel hybrid noninvasive imaging systems for studying the pregnant
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 8 hours ago
to process the notes, EHR data as well as the images to advance the aims of the project. Mentors for the candidate will include Dr. Ashok Krishnamurthy and Dr. Matt Satusky from RENCI, Dr. Emily Pfaff from
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remaining. • Strong background in in vivo behavioral assays, electrophysiology, and/or imaging techniques. • Solid publication record in peer-reviewed journals. • Experience with stereotaxic surgery and
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Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based on autofluorescence (AF) imaging