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Simon Fraser University | Burnaby (Cascade-Schou / Douglas-Gilpin), British Columbia | Canada | about 1 hour ago
, Canada Simon Fraser University’s Department of Mathematics invites applications for a postdoctoral fellowship in scientific machine learning and numerical methods for weather prediction. The fellow will
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; • Implementing and testing proposed methods in numerical case studies, with possible applications in quantitative finance, stochastic control, or risk management; • Preparing research manuscripts
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Mathjobs.org | 29 days ago
Join an ARC-funded research project developing numerical methods to simulate the flow of dense viscous liquids through a deformable granular mush. The project aims to improve fluid-dynamical
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Join an ARC-funded research project developing numerical methods to simulate the flow of dense viscous liquids through a deformable granular mush. The project aims to improve fluid-dynamical
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methods for compact binary systems. Specifically, the scholar will conduct research in numerical relativity by extending the Einstein Toolkit, BlackHoles@Home, and the superB project to perform state-of
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, Energy Systems, Computational Mechanics or a related discipline . You will have experience with numerical modelling and/or data-centric methods and a keen interest in applying these to energy and
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project developing numerical methods to simulate the flow of dense viscous liquids through a deformable granular mush. The project aims to improve fluid-dynamical understanding of how magmatic ore
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theory, the postdoctoral researcher will construct an adaptive algorithm, implement and test the methods in the established ice-sheet modelling tool Elmer/Ice, and carry out numerical experiments with
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/or data-centric methods and a keen interest in applying these to energy and infrastructure challenges. You should be able to demonstrate experience in numerical modelling of coupled thermal
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python), computational mechanics and fluid dynamics, numerical methods, machine learning, and the finite element method. In this position, the candidate will develop data-driven multiscale/multiphysics