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the following: Magnetic resonance imaging Python, MATLAB, C/C++ Excellent writing skills, as evidenced by first author publications Exposure to clinical MRI in general, with knowledge of brain tumor MRI
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. The successful candidate will collect electrophysiology and imaging data and analyze neuronal activity and behavior to better understand how neural circuits control behavioral performance. Applicants should have a
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Post-Doctoral Position - Oncology & molecular biology & radiology imaging - (210001I2) Exciting positions for postdoctoral fellows or research specialists in chemistry or in molecular biology
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, computer science, biomedical engineering, biology, or other biomedical discipline. Previous research using 2-photon imaging, slice electrophysiology, optogenetics, rodent behavioral analysis, calcium imaging
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research project using cutting-edge approaches in whole-cell patch-clamp electrophysiology, in vivo calcium imaging, and animal behavioral tests. • Employ advanced neural circuit dissection methods (e.g
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: Exempt Work Schedule: Monday – Friday, 8 a.m. – 5 p.m. Summary The Translational Imaging Group (TIGr) in the Department of Radiology at Texas Children’s Hospital and Baylor College of Medicine is seeking a
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
data collection of mammograms, and pathology and radiology images for potential development of a Virtual Tumor Board. The selected candidate is expected to work in a team environment together
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treatment plans, enhance patient outcomes, and innovate radiation therapy techniques. Job Duties 1- AI-Modeling for Treatment Planning: Develops, optimizes, and validates AI-based models to assist in image
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Department:Radiology Salary Salary:$50,004.00 to Commensurate Position Details Full/Part Time Status:Full Time Percent Time:100% Position Description: Post-Doctoral Research Fellow in Quantitative Brain Imaging The MR
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encryption and zero-knowledge proofs to protect personal data. • Create solutions for real-time detection of model tampering, including mechanisms for identifying and preventing personal data exposure