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Field
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to experimental data from photon-counting or time-resolved detectors. Experience with Bayesian methods, uncertainty quantification, or real-time data processing. Familiarity with distributed computing or HPC
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within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical questions and is aimed at novelty, understanding of physiology and
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the programme’s objectives. If interested, please send your CV, an abstract of your proposal, and a letter of commitment to apply to our institution to m.dominguez@umh.es . Where to apply E-mail m.dominguez@umh.es
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Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object tracking, 3D reconstruction, super-resolution. Have a passion on
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within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical questions and is aimed at novelty, understanding of physiology and
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molecular structures capable of transferring electrons and interacting with light. Such assemblies also have applications in biomedicine. The primary objective is to develop computational methods, using deep
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movements can be preceded by slow movements lasting from several days to several years. These movements can be detected and tracked by satellites, either using radar or optical sensors. Since 2016, data from
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outputs, including papers for submission to peer reviewed journals under the direction of the Principal Investigator or Grant-holder. To assist with the development of research objectives and proposals
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for the position of Postdoctoral Associates. FIU¿s Post-Doctoral Associate Program is designed to recruit outstanding Ph.D. applicants to assist the organization in meeting its objectives. The applicant¿s Ph.D. must
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for a Postdoctoral Scholar. The Scholar will conduct research on Bayesian spatiotemporal modeling methodology under the direction of Professor David Dunson at Duke on developing novel models motivated by