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Position Description The New Jersey Institute of Technology is searching for a Post Doctoral Research Associate to work on problems in the intersection of inverse problems, analysis, and computational
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of Python programming and a deep learning framework, preferably PyTorch. Solid knowledge of image processing, inverse problems, and machine learning. Significant research experience, demonstrated by quality
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at familiar issues and deliver new tools to solve complex problems. About the role The Department of Mathematics at King’s College London invites applications for two postdoctoral research associate positions
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Experience with super-resolution ultrasound, US localization microscopy, photoacoustic imaging, elasticity imaging, pulse encoding, solving inverse problems, machine learning, AI, SolidWorks, 3D printing FLSA
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theoretical and intellectual value, the research will benefit many applications, such as biomedical therapy, remote sensing, antenna/filter theory, and inverse problems in physics and engineering. Good working
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at familiar issues and deliver new tools to solve complex problems. About the role The Department of Mathematics at King’s College London invites applications for two postdoctoral research associate positions
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to develop specific surrogate models for BUQ. Surrogate models of the underlying PDEs in the Bayesian Inverse problem (or BUQ) are sough, as Physics informed interpolators, multiscale models, PINNs etc
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. Desirable extras include experience with wind‑energy structures, machine‑learning/inverse‑problem techniques and prior industrial collaboration. Further details about the role requirements are available in
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processing, inverse problems and data science with emphasis on analysis, optimization, numerics and algorithmic solution Collaboration in interdisciplinary cooperation projects and third-party funded projects
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. The specific topic of the project falls in the intersection of statistical signal processing and applied mathematics and is in particular concerned with optimal transport for inverse problems. Optimal