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Master's degree in Mathematics or equivalent (to be completed before the start date). You have a strong background in numerical analysis and partial differential equations, and you have experience in
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numerical analysis and partial differential equations, and you have experience in scientific computing or programming (e.g., Python). Knowledge of uncertainty quantification or inverse problems is welcome but
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solid foundation in numerical analysis, partial differential equations, and linear algebra. Theoretical and Practical Balance: You enjoy developing and analyzing mathematical theory, but also have the
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for implementing ONNs. Modeling, simulate and benchmark different computing tasks such as combinatorial optimisation tasks and solving partial/ordinary differential equations with ONNs. Design and tapeout ONN chips
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in Random Media’. The PhD position focuses on the dynamical behaviour of stochastic partial differential equations (SPDEs). In particular, we will consider the impact that noise terms have on patterns
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for implementing ONNs. Modeling, simulate and benchmark different computing tasks such as combinatorial optimisation tasks and solving partial/ordinary differential equations with ONNs. Design and tapeout ONN chips
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(agent-based modeling, differential equations) or machine learning tools. Good programming skills in one of the following programming languages: R, Python, MATLAB, or similar; Excellent English language