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dynamics, mechanical engineering, computational physics, applied physics, mathematics, geophysics, or related subject areas. Experience with programming languages such as Fortran, C/C++, MATLAB, or Python is
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engineering, computational physics, applied physics, mathematics, geophysics, or related subject areas. Experience with programming languages such as Fortran, C/C++, MATLAB, or Python is advantageous
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4 Jun 2024 Job Information Organisation/Company University of Twente (UT) Research Field Physics Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 15 Jul 2024 - 21:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is the job...
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in fluid dynamics, mechanical engineering, computational physics, applied physics, mathematics, geophysics, or related subject areas. Proficiency in programming languages such as Fortran, C/C++, MATLAB
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candidate will work under the supervision of Dr. rer. nat. José A. Iglesias Martínez and prof. dr. Christoph Brune, as part of the group “Mathematics of Imaging and Artificial Intelligence” (MIA
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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
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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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candidate who is driven by curiosity and has: -or will shortly acquire-, a Master degree, or equivalent, in Technical Medicine, Biomedical Engineering, (applied) Mathematics, Computer Science or a related
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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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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