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postdoctoral position is available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research at the intersection of Geometry
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modelling, CFD mesh generation in complex 3D geometries. Proficient in handling large data sets and the ability to analysis and interpret results. Experience in commercial CFD package such as ANSYS package
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postdoctoral position is available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research at the intersection of Geometry
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key areas: designing hybrid and immersive experiences and understanding technology’s role in shaping human interaction with the built environment. Andrew Witt, Computational Geometry Lab
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tokamak geometry on turbulent regimes and power-load distribution in double-null configurations Build-up a comprehensive database based on these simulations and analyze data Job Requirements: PhD degree in
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stochastic analysis; differential geometry and geometric analysis; algebraic and geometric topology; algebra, number theory and cryptography; dynamical systems; analysis and nonlinear/stochastic partial
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: The successful applicant will be responsible for: Obtaining rigorous mathematical results at the interface of optimization, geometry, and data science Designing, implementing, and testing algorithms Engaging in
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the US National Science Foundation. The overall aim of this project is to determine the geometry and plumbing of the hydrothermal system of an active submarine arc volcano and its relationship with
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optimization of a personalized cooling system by simulating various geometries, materials, and thermoelectric module configurations using COMSOL. • Develop and execute parametric studies to evaluate system
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approaches. We will use evolutionary biology methods across four insect species of diverse sizes and geometries and spanning over 400 million years of evolution. Indirect evidence suggests early cardioblasts