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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
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Torbein Kvil Gamst 26th April 2026 Languages English English English Faculty of Science and Technology Postdoctoral Research Fellow in Machine Learning Apply for this job See advertisement
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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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York University Overview The NYU Entrepreneurial Institute's Assistant Director of Research Commercialization leads programs and initiatives designed to accelerate the commercialization of deep
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Postdoctoral Scientist -Surgery Chair, Ruppin Lab Job ID 13729 Location Los Angeles, California, United States Job Category Academic / Research Date posted 03/19/2026 Apply Now Save Job Employees
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-changing technologies. Life-changing careers. Learn more about Sandia at: https://www.sandia.gov *These benefits vary by job classification. What Your Job Will Be Like: We are seeking a Postdoctoral
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of the following areas: Wireless and satellite communications AI/ML for dynamic networks including Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models
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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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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow in Machine Learning for Computational Pathology, Medical Imaging, and Clinical Text Analysis Department Bashashati
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
. Preferred Qualifications, Competencies, and Experience Programming skills (e.g., python, bash scripting, Fortran, C++ and CUDA), expertise in computational modeling (such as Deep Learning, Molecular Dynamics