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Field
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Mathematical AI/Machine learning/deep learning Computational topology/geometry/combinatorics Candidates are required to have a Ph.D. in Mathematics, mathematical related field or an equivalent area before
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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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encouraged to apply. The successful candidate will be expected to establish a vigorous, externally funded research program characterized by originality and long-term vision, while also contributing
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are non-Lorentzian field theory and gravity (e.g. Galilean, Carrollian, and Lifshitz geometries), non-AdS extensions of gauge/gravity dualities (e.g. non-relativistic holography, Carrollian perspective
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research spans Computational Mathematics, Nonlinear Mathematics including Nonlinear Dynamics and Waves, Mathematical Biology, Physical Applied Mathematics, Probability, and Statistics. The Department
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outreach and service for Department and College initiatives, especially for underserved communities and high-needs areas. Contribute to program design, development, and evaluation at the undergraduate
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percentages; use algebra, geometry and trigonometry, 10) instruct others to perform a task, 11) provide guidance and expert advice about technical systems or processes, 12) locate, disable and enable electrical
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advanced vehicle concepts and better control systems. Actively changing the geometry of the aerodynamic lifting and control surfaces, enables reconfiguration of vehicle structure in real-time to optimized
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apprenticeship program with five years of journeyman rigger experience or 10 years combined documented experience in the rigging industry. Preferred Qualifications: Possess the ability to work collaboratively with
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astrophysical free boundaries. Responsibilities include running high-resolution GPU-accelerated simulations on exascale computing systems, developing and applying geometric measure theory tools to quantify