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on calibration in deep learning at the Pervasive Systems Research group, Department of Computer Science, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente in
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research group MISD project Sample of our work on sparsity Requirements Specific Requirements The ideal candidate we are looking for has: A PhD in Computer Science, Mathematics, Computational Neuroscience
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efficiency of deep neural networks. Investigate the mathematical underpinnings of sparsity in deep learning and its effects on learning dynamics, and generalization. Implement and benchmark sparse training
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original research on sparse-to-sparse training techniques, exploring new frontiers in algorithmic development for DRL. Investigate the mathematical underpinnings of sparsity in deep reinforcement learning
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: Good programming skills. Experience with machine learning libraries (e.g., TensorFlow, PyTorch, Jax) is helpful; Strong mathematical background, in particular statistics & probability, linear algebra
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, applied mathematics, materials science, or related areas Experience with numerical methods for solving partial differential equations; Previous experience with multi-phase materials and transport phenomena
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. Requirements Specific Requirements Obtained a MSc degree in a relevant field such as civil engineering, mechanical engineering, computational physics, applied mathematics, materials science, or related areas
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engineering, mechanical engineering, computational physics, applied mathematics, materials science, or related areas; Good knowledge of fluid-coupled particulate systems in slow and/or fast motion; Sound
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completing, a Master’s degree in Civil Engineering, Geophysics, Geology, Geotechnics, Earth Sciences, Applied Mathematics, or a related field. Possess programming skills, which would be a significant advantage
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-edge research at the intersection of computational mathematics and its applications in physics, engineering, and material sciences, with a specific focus on addressing direct and inverse problems in