Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
-
Field
-
to work with Professor Elena Beretta on inverse problems for nonlinear partial differential equations arising in the life sciences and medical imaging. The successful applicant will contribute to a
-
, inverse problems and data science with emphasis on analysis, optimization, numerics and algorithmic solution • Collaboration in interdisciplinary cooperation projects and third-party funded projects
-
automated finite element and inverse-identification workflows that learn constitutive parameters from a single informative test through Bayesian optimization, together with quantification of measurement
-
acquiring full-field deformation data using three-dimensional digital image correlation (DIC). On the numerical side, this involves building automated finite element and inverse-identification workflows
-
and selectivity, shape 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
-
geometry, number theory, data. * Qin Li: Scientific computing, machine learning, inverse problems. * Hanbaek Lyu: Combinatorics, probability, optimization, and machine learning. * Laurentiu Maxim
-
King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 8 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
-
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
-
frameworks scientific machine learning, inverse problems, differentiable simulation, or physics-based modelling computer vision, biological image analysis, microscopy data, or other noisy high-dimensional
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 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