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methods for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi
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processing, characterization, and mechanical testing. A campaign layer, driven by Bayesian optimization, decides which experiment to run next. The postdoc will own the system architecture below that layer
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processing, characterization, and mechanical testing. A campaign layer, driven by Bayesian optimization, decides which experiment to run next. The postdoc will own the system architecture below that layer
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 8 hours ago
to assess blood flow, function, diffusion, and CSF flow are of particular interest. The candidate will also develop or improve software for the analysis of these unique MRI data, such as object
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and written communication skills in English Background in biomarker analysis and/or compound specific isotope analysis and/or archaeometric dating techniques and Bayesian statistics. Personal
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PhD position: From the Origin of Life to New Materials – Nanopore Detection of Nanocluster Heterogeneity with Single-Molecule Resolution (P2604) 100% position for 4 years, earliest starting date: 1
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, object detection, feature extraction, image registration, or change detection. Experience working with satellite, aerial, remote-sensing, or geospatial imagery. Experience developing or adapting
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scientific discovery, collaborative working experiences, and professional mentorship and support. The postdoctoral associate will be affiliated with the Center for Avian Population Studies (CAPS, https
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a human partner to solve a spatial problem (e.g. 3D puzzle). The tasks to be carried out are: (i) scene understanding: detection of objects and relations between objects, and use of these relations to
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psychiatric outcomes. The successful candidate will bring demonstrated expertise in Bayesian statistical methods and causal inference frameworks (e.g., Bayesian causal networks, cross-lagged panel