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rotation angles. These are two inverse problems, each with its own sampling requirements. The questions we want to answer: What is the minimum number of diffraction patterns and projections needed to
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Mathjobs.org | about 1 month ago
discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical
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discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 23 hours ago
Qualifications: Expertise in the development of seismic processing algorithms, high-performance computing, and/or large-scale inverse problems; Experience in developing open-source software and a track
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datasets. Background in inverse problems or physics-informed machine learning. Exposure to scientific imaging applications (for example, biological, environmental, or materials science). Job Family
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the technical approach, explore AI-assisted inverse design and lead publications. Integration into a working sensor prototype will be a shared effort with the PI, PhD researchers and technical staff
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geometry, number theory, data. * Qin Li: Scientific computing, machine learning, inverse problems. * Hanbaek Lyu: Combinatorics, probability, optimization, and machine learning. * Laurentiu Maxim
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frameworks scientific machine learning, inverse problems, differentiable simulation, or physics-based modelling computer vision, biological image analysis, microscopy data, or other noisy high-dimensional
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
, QUDA , Grid) and GPU computing. Experience with statistical analysis of large Monte Carlo datasets, including inverse-problem and Bayesian methods. Experience using DOE computing allocations ( NERSC
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observable reconstruction. Experience with uncertainty quantification, covariance modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model