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-invasive diagnostic test for motor neuron disease (MND). You will join a multi-disciplinary team of neuroscientists, MR physicists, neuroradiologists and computer scientists and will be primarily responsible
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. Funding Notes Full details of how to apply can be found at the following link: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying Applicants can also apply for a scholarship from the University
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LevelPhD or equivalent Skills/Qualifications - Hold a PhD degree in Communication Sciences and related fields. - Proven experience in immersive audiovisual content production processes - Proven experience in
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microscopy techniques (e.g., spinning-disk confocal microscopy, light-sheet microscopy) analysis of large imaging datasets using advanced image processing and segmentation methods computational and statistical
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Automatic Object and Behaviour Recognition in Video/ Image Sequences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application Deadline
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/ Please apply for this project using this link: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying Funding Notes Externally or self-funded students only View DetailsEmail EnquiryApply Online
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and take ownership of work Interest in AI, machine learning, image/audio processing Where to apply Website https://www.timeshighereducation.com/unijobs/listing/408369/research-engineer-r… Requirements
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students pursuing DVM degrees, veterinarians in internship and residency programs, and MS and PhD students. Learning to better understand, diagnose, and treat disease takes place in some of the most advanced
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 23 days ago
. - COMPETE2030-FEDER-00682600, Operation No. 15722, funded by COMPETE2030 and national funds (FCT) under Notice no. MPr-2023-12. Host Institution: i3S - Instituto de Investigação e Inovação em Saúde, Porto
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information about our institute here: https://www.fz-juelich.de/en/ias/ias-8 Your Job: Develop 3D+t image reconstruction methods in a cell microscopy setting using image sequences as well as focus stacks