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analysis of nuclear cardiology data using novel algorithms and machine learning techniques, and on the development of integrated motion-corrected analysis of positron emission tomography (PET)/computed
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consolidation of precursor metal materials into metal 3D parts and structures, enabling complex geometry and material designs. The fundamental physics of these processes is relatively complex, involving various
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., biomarkers, metabolites) must be evaluated using digital twins of breath device prototypes. Our digital twins are based on simulations using computational fluid dynamics (CFD) and computational fluid and
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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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research specialization. Experience in medical imaging, image processing, image reconstruction, machine learning and/or computer vision. Proficiency in mathematics, medical statistics, and 3D geometry
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regimes, and accurate geometry- and biochemistry-based trajectory analyses. However, detailed molecular dynamics simulations are often too time-consuming to become the basis of computational measurements
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Computational electromagnetics; Integral equations; Numerical algorithms; Fast multipole method; Field solvers; Eligibility Citizenship: Open to U.S. citizens Level: Open to Postdoctoral applicants Stipend Base