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Field
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of the nonlinear structural performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators
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or magnetometry • Numerical and analytical approaches (e.g., MATLAB, Mathematica, etc.) Training and resources Modelling component: The successful candidate will develop several computational skills in terms
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
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Experience implementing mixed-integer, conic or nonlinear programming and modelling frameworks (JuMP, Pyomo, Yalmip, GAMS, etc) Experience with unbalanced distribution system analysis tools (e.g., OpenDSS
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these questions, the project will involve computational modelling and experimental work, jointly supervised by Martin van Hecke and Yoeri van de Burgt. With this research, we aim to redefine physical computation
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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, Applied Mathematics, or a related field. Strong foundation in computational modelling & numerical simulations The laboratory The Decision and Bayesian Computation (DBC) – Epiméthée (EPI) laboratory
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attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security
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Research: Visual Exploration and AI Prediction Modeling of Real-Life, Multi-Modal Data” as a PhD-Position in machine learning. You will work alongside leading experts at the Computational Imaging Research
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models