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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 11 hours ago
cutting-edge machine (deep) learning numerical methods for wave-equation-based processing, imaging, and inversion. Wave phenomena are ubiquitous in science, and they extend to objectives ranging from
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framework for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI
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integrated semiempirical density-functional tight-binding and deep-learning neural networks. These methods will be integrated into the next-generation Amber software suite used worldwide. The project is to
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available data, which is heavily biased toward a small number of well-studied epitopes, and (iii) the lack of deep learning (DL) approaches specifically designed to capture the unique characteristics
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or a related discipline; demonstrable experience with scientific programming (preferably in Python, including deep learning frameworks such as PyTorch); affinity with medical image analysis and
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Infrastructure? No Offer Description Are you passionate about combining the directed evolution of diverse biomolecules with deep learning approaches and contributing to the development of better (bio
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marine datasets, identifying key process drivers, developing deep learning models for spatiotemporal prediction, and applying them in Baltic Sea case studies. Applicants should have a relevant
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preferred, please specify the type: Preferred Experience: Experience in deep learning, machine learning and medical imaging processing Programming experience: Python, MATLAB , SPSS , Shell. Experience
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preferred, please specify the type: Preferred Experience: Experience in deep learning, machine learning and medical imaging processing Programming experience: Python, MATLAB , SPSS , Shell. Experience
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foundation models or foundation models for structured data is particularly relevant to this position. Key qualifications include: Strong research experience in deep learning and foundation models