18 Mathematics PhD positions at University of Twente (UT) in Phd

PhD position (f/m/d): Pioneering simulations of offshore wind farm turbulence

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

PhD position on analysis of geometric machine learning methods

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

PhD position on Fast and Accurate Integral Equation Methods for Computational Problems in Wave Phenomena

-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

PhD position Reinforcement learning for digital finance

: Good programming skills. Experience with machine learning libraries (e.g., TensorFlow, PyTorch, Jax) is helpful; Strong mathematical background, in particular statistics & probability, linear algebra

PhD position on Cardiorespiratory Modelling and Simulation in Critical Care

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

PhD position on Calibration in Deep Learning for Zero Downtime in Cyber-Physical Systems

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

PhD position on Sparse Training for Energy-Efficient Deep Learning

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

PhD position on Sparse Training for Deep Reinforcement Learning

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

13 Phd Positions on the EU Horizon 2020 Marie Sklodowska-Curie Project POSEIDON

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

PhD Position in Machine Learning for Digital Fincance

, Engineering, Information Technology, Mathematics, Computational Science or related fields; A strong passion and outstanding skills in data science and experience working with Python; Knowledge of quantitative