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garments into recycle, reuse, and manual-review streams; this PhD project tackles the core challenge of designing and optimizing a high-throughput hyperspectral imaging system, fused with complementary
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engineering and science, as well as PhD education in computer technology. The Mohns Center for Innovation and Regional Development researches innovation and offers master's education in innovation and
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; experience of working on biological systems and/or craniofacial system; experience in image processing and material characterisation. The post is initially funded for 3 years and will be subject to standard
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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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the presence of photosensitizers. Conduct imaging studies in giant unilamellar vesicles (GUVs) to validate the impact of deuterium content in the lipid membrane to mitigate the photosensitizer activity. Conduct
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understanding of the polymer network morphology and how it relates to the laser process parameters. Specifically, you will carry out high resolution Raman imaging on laser written polymer networks with liquid
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challenges and national priorities through the creation of over 100 new faculty positions in some of the most promising – and critical – fields of research. Applicants must have a PhD, ScD, MD, or equivalent
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neuroimaging experiments, proficient in image processing and programming paradigms. The successful candidate will contribute to ongoing multidisciplinary research and play an active role in developing novel
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largest biomedical research institute under one roof. Our world-class scientists and staff collaborate on vital research to help prevent, diagnose and treat illnesses such as cancer, heart disease
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how