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to staff position within a Research Infrastructure? No Offer Description Thousands of students across many technical programmes are taught mathematics every year at the University of Twente, by
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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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-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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, applied mathematics, or similar areas. Affinity with precision mechatronics is advantageous. You have affinity with design of precision mechanism and numerical analysis,(flexible) multibody dynamics
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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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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