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experience with HHG sources are a plus but not critical. Knowledge of programing languages such as Python (and with finite element simulations specifically) would be also valuable.
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, engineering, materials science, maths, or computer science), or equivalent experience Experience with uncertainty quantification or error analysis Familiarity with numerical methods (e.g., Monte Carlo, Finite
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a variety of approaches, such as stochastic processes, kinetic theory, variational analysis, finite element methods, and data-driven techniques. The Vienna School of Mathematics doctoral program
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astrophysics, and uses a variety of approaches, such as stochastic processes, kinetic theory, variational analysis, finite element methods, and data-driven techniques. The Vienna School of Mathematics doctoral
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demonstrated ability to communicate and interact with a diverse range of stakeholders and students. Demonstrated knowledge in Quasi-Monte Carlo methods and/or finite element analysis and/or machine learning is
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experience · Be a proficient programmer, including in Python, with good coding habits; experience in NEURON, COMSOL, other finite element modeling software, and / or git would be an asset; experience
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. Assets (Nonessential): Experience with FEA (Finite Element Analysis) and CFD (Computational Fluid Dynamics). Excellent skills with Solidworks, Inventor, and/or Siemens NX, or other solid modelling software
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demonstrate these in their application materials Familiarity with numerical methods for PDEs (e.g., finite difference or finite element methods) Experience with tissue simulations and/or HPC is a plus Interest
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biology, or applied mathematics Documented experience in C++ programming and solid software engineering fundamentals Familiarity with numerical methods for solving PDEs (e.g., finite difference, finite
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. The central aim of these projects is efficient computational method for wave propagation on complex geometry. We will use a novel and unconventional finite element method based on the Galerkin difference