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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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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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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
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companies. The research will integrate techniques of numerical analysis and structure-preserving algorithms to generative modeling in AI. It will build upon the work done at IMF and SINTEF in this field. We
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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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9.4. The Interview (INT), with the duration of 20 minutes, will be classified on a numerical scale from 0 to 100 points, applying the following parameters and criteria evaluation, represented in
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project. Computational mechanics PhD projects PhD 1: Rolling contact fatigue in green bearing steels – numerical microstructural modelling [SKF; Ron Peerlings] PhD 2: Predictive analysis of edge crack
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extension for another 6 months. It is based in the Schulenburg group (https://evoecogen-kiel.de/ ). The PhD project aims at understanding how evolutionary principles can be harnessed to constrain bacterial
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-driven modelling. Experience with numerical modelling, simulation, optimization, control, or engineering-data analysis. Good programming skills in Python, MATLAB/Simulink, or a comparable scientific