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–experimental feedback loop central to the project. Subject description Recent breakthroughs in deep learning–powered protein design, recognized by the 2024 Nobel Prize in Chemistry, have enabled the creation
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variational models and deep learning techniques. You will implement and validate reconstruction algorithms, ensuring their performance, robustness, and efficiency for clinical application. You will participate
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 19 days ago
pending funding and performance. Minimum Education and Experience Requirements PhD in Computer Science, Computing, Statistics or Data Science, or related disciplines Required Qualifications, Competencies
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
pending funding and performance. Minimum Education and Experience Requirements PhD in Computer Science, Computing, Statistics or Data Science, or related disciplines Required Qualifications, Competencies
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mathematics or a related field strong background in image processing and analysis, including deep learning (e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong
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developers and many more, that are focused on bringing data, machine learning and statistical modeling into the products that we build for our clients or internal users. The data scientists in INGA furthermore
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expertise in deep learning including experience with libraries such as Pytorch Have strong communication, presentation and writing skills; Enjoy working in a multidisciplinary research environment; Are highly
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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anthropogenic factors using deep learning and vision transformer models, (2) Incorporating past factor trends for more realistic predictions under the non-equilibrium hypothesis, (3) Leveraging transfer learning
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on the development of deep learning methods for reconstruction and physics analysis of the ATLAS experiment data. The successful candidate will develop innovative analysis methods for the reconstruction or the physics