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assessment. You will be provided with access to various engineering and computation toolsets along with the high-performance computer. A good background in numerical methods and computational platforms is
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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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-method study of human cultural adaptation to environmental challenges with application to invasive plants and tick-borne disease threats among woodland owners with supervision from Dr. Waring and in
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Pacific Institute for the Mathematical Sciences | Northern British Columbia Fort Nelson, British Columbia | Canada | about 1 month ago
discretizations and/or machine learning methods. The ideal candidate should have a strong background in numerical analysis, scientific computing, and/or scientific machine learning. We are particularly interested
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, or a closely related field by the start date Strong background in many-body quantum theory and/or quantum computing Experience with numerical methods or quantum simulation frameworks is desirable
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
Geotechnical Engineering, Civil Engineering, or a related field, and should demonstrate strong expertise in at least two of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian
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record in theoretical and/or computational mechanics Knowledge, Skills, and Abilities Excellent analytical and mathematical skills Proficiency in numerical analysis using finite element method (FEM
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applications for a Postdoctoral Associate position in the area of physical oceanography. The successful candidate will use numerical simulations to study the dynamics and meridional heat transport of the Agulhas
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materials with applications in heterogeneous catalysis, energy conversion and electrochemistry. By combining unique synthesis methods, state-of-the art tools for experimental characterization and advanced
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the following areas: numerical analysis of PDEs (with deterministic and/or stochastic methods), Gaussian Random Fields, mathematical foundations of deep learning, functional analysis and measure theory