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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
hand-eye coordination, and a high level of concentration to do challenging techniques. Special Instructions For information on UNC Postdoctoral Benefits and Services click here Quick Link https
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. To apply, visit https://northeastern.wd1.myworkdayjobs.com/en-US/careers/job/Boston-MA-Main-Campus/Postdoctoral-Research-Fellow--Moderna---Clinical---Quantitative-Pharmacology_R139074 Copyright 2025
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computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability to communicate scientific results clearly through
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of deep learning in many disciplines, particularly computer vision and image processing. Consequently, coding architectures based on deep learning and end-to-end optimization have been proposed [Ding 2021
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learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image analysis, that is medical images that evolve over
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AI / Machine learning / Computational Oncology lab:Our work is translationally focused, towards realizing our vision of developing new approaches for fast and low-cost prediction of patient response
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algorithms for ultrasound simulation, imaging, and quantitative analysis Customize machine learning / deep learning methods for image reconstruction Conduct human studies of the algorithms and techniques in
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algorithms for ultrasound simulation, imaging, and quantitative analysis Customize machine learning / deep learning methods for image reconstruction Conduct human studies of the algorithms and techniques in
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associate will work both independently and collaboratively to develop and apply novel deep learning algorithms and/or computational chemistry methods for small-molecule drug discovery targeting RNA
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biologically-inspired deep learning and AI models (NeuroAI). The computational models we work with include vision deep learning models (including topographical, recurrent, or developmentally inspired models