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of Health Science and Technology, one or more PhD stipends in Unsupervised Learning for Medical Image Analysis are available for appointment from November 1, 2026, or as soon as possible thereafter
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8 Sep 2026 Job Information Organisation/Company FUNDACION DE INVESTIGACION BIOMEDICA HOSPITAL 12 DE OCTUBRE Research Field Biological sciences Researcher Profile Other Profession Positions PhD
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on exploiting and enriching an existing experimental database. The candidate will contribute to the design, execution, and analysis of in operando intrusion experiments, including the acquisition and processing
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Medical Computer Vision Experience with Large Language Models and Generative AI Experience working with real-world EHR and imaging data Experience working with PACS and the DICOM standard Preferred
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Memorial Sloan Kettering Cancer Center | New York City, New York | United States | about 2 hours ago
. Our investigator group, led by Drs Santosh Vardhana (tumor immunology), Sohrab Shah (computational oncology) and Vivian Strong (gastric cancer surgery) has established a platform that enables
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supplements on an annual basis. The recruitment process Further information on the recruitment process at University of Copenhagen can be found here: https://employment.ku.dk/faculty/recruitment-process/ An
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: https://www.tarvinlab.org/ Qualifications Basic qualifications (required at time of application) PhD (or equivalent international degree) or enrolled in a PhD (or equivalent international degree) program
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community of over 500 staff and more than 300 PhD students engaged in cutting edge research and collaboration. Further information can be found on our website https://www.ucl.ac.uk/biosciences This is an
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motivated, collaborative and service-oriented PhD-level specialist who will work as part of the ABBE team. The successful candidate will support users from initial experimental planning through image
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-field deformation data using three-dimensional digital image correlation (DIC). On the numerical side, this involves building automated finite element and inverse-identification workflows that learn