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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian-Lagrangian (CEL), Material Point Method (MPM), or advanced Finite Element Methods). Physical modeling of tunnel
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Deadline: 2025/12/31 11:59PM * (posted 2025/12/01, listed until 2026/01/01) Position Description: Apply Position Description The MIT LIGO Laboratory is seeking a Postdoctoral Associate interested in
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mathematics. An important aspect of the ongoing research is solving stochastic partial differential equations on surfaces, e.g., with surface finite element methods. The following requirements are mandatory: A
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structure-preserving discretization algorithms (a refinement of finite-element analysis compatible with exact geometric, topological, and physical constraints) with artificial neural networks for achieving
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computational fluid dynamics (CFD), cardiovascular modeling, or biomechanical growth and remodeling. Demonstrated experience with numerical methods (e.g., finite element method), programming languages (C
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computational models (e.g., Convection-Diffusion Equations, Finite Element Methods, Computational Fluid Dynamics) to investigate cerebral blood flow (CBF), neurovascular coupling (NVC), and neurovascular system
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian-Lagrangian (CEL), Material Point Method (MPM), or advanced Finite Element Methods). Physical modeling of tunnel
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encompasses various in silico modeling techniques to cover the multiple spatial scales, i.e. from molecular detailed to multicellular high-level regulatory networks, up to tissue level finite element models
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for the solution of partial differential equations. Research experience with integration of ROM and the Finite Element method is a plus. Demonstrated programming skills (Fortran/C++/Python/Julia), preferably in
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of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and uncertainty quantification. The position comes with a