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. Qualifications: You should have (or be close to achieving) a PhD degree. Background within computational methods for inverse problems, ideally tomography. Experience with development of numerical implementations
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and prototype imaging system design Excellent programming skills, Medipix/Timepix detectors, analytical models, forward, and inverse problems, and prototype system development for clinical translation
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power (CSP) systems optimization, computational heat transfer and radiative transport using sophisticated numerical modeling and machine learning approaches for forward and inverse problems in radiation
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on regional to global scales through inverse analyses. She/He will also work on other issues related to global atmospheric chemistry, air quality, and model development. Responsibilities include initiating and
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R240260 Posting Link https://www.ubjobs.buffalo.edu/postings/54003 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary Grade E.89 Posting Detail Information
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at the Computational Science Initiative (CSI), within the Brookhaven National Laboratory. The selected candidate will collaborate on solving inverse problem, relevant for interference lithography process, by deploying
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Organisation Job description The advent of modern machine learning (ML) methodology is accelerating scientific progress by streamlining research across disciplines. In chemistry, it enables
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containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling
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of linear algebra and numerical optimization Understanding of statistical modeling and inverse problems is desirable Experience with programming languages like Python, MATLAB, or C++ Joy in dealing with
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focus on deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning