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/Machine learning/deep learning Computational topology/geometry/combinatorics Research Associates will be affiliated with Professor Guowei Wei , who will direct their research activities. The research
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Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal
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mathematicians with research interests in common with the active research areas at Rice; particularly, algebraic geometry and number theory, analysis, differential geometry, dynamics, geometric analysis, geometric
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Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal
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damage in realistic geometries; investigate surrogate models; make original research contributions to the PSFC program; and produce work for publication, present work at scientific conferences, and mentor
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on computational modeling using Monte Carlo techniques, as well as modeling of radiotheranostics, modeling of alpha particles, Auger electrons, and conversion electrons for the effects of DNA damage. Required
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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