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moving through different fluids. In this project, we are interested in developing moving mesh finite element methods for their dynamical simulation. We aim to produce efficient, accurate and robust
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and carry out finite element method (FEM) simulations. Our developments focus on higher efficiencies, more cost-effective manufacturing processes and materials, improved long-term stability and new
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, finite element analysis, computational fluid dynamics, to big data and AI workflows. As part of a collaborative, forward-thinking environment, you’ll receive mentorship to solve complex problems, provide
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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 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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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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, 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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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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numerical. Analytical results for classical problems of high symmetry also will be presented to serve as a basis for comparison and validation. Software: A finite element and computational transport tool