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Field
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range of topics in numerical analysis and applied mathematics, including: Approximation Theory & Numerical Integration Mathematics for Data Science & AI Combinatorial Optimization Stochastics
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the field of mathematical modelling and numerical methods for two-phase flow. You write and publish research articles, contribute to national and international conferences and build an international network
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PhD Scholarship Non-persistent rock discontinuities, rock bridges, and rock mass stability: numerical modelling and analysis. Water flow in rock discontinuities, discontinuity mismatching, and shear
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discipline. Desirable Experience in machine learning, deep learning, data analysis, numerical modelling, or scientific programming (such as Python, MATLAB, or R) is desirable. Knowledge of hydrodynamic
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data (e.g. instrumental, laboratory, environmental, numerical and categorical data, text, geospatial data or time series); c) Experience in Python programming and in data analysis and machine learning
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. Chris Stolk (University of Amsterdam). You will interact with researchers with backgrounds in inverse problems, numerical analysis, scientific computing and imaging, and with project partners working
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systems are generally ill-conditioned. The project sits at the intersection of classical numerical analysis, scientific machine learning and computational chemistry. Based on regularization techniques and
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systems. The mathematical disciplines involved are mathematical modeling, numerical analysis, and scientific computing. Your tasks will involve: Conducting research on uncertainty quantification in
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Danfoss, you will combine thermofluid modelling, reduced-order multiphysics methods, and nonlinear rotor dynamics analysis to develop predictive modelling tools that support the industrial design of
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(CWI) and co-supervised by Dr. Chris Stolk (University of Amsterdam). You will interact with researchers with backgrounds in inverse problems, numerical analysis, scientific computing and imaging, and