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research design, involving semi-structured interviews with key informants from micro food firms engaged in food production and processing. A network picture approach will also be employed to capture
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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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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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-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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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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/ 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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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 24 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
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project. The project will also employ a PhD student at Lund University, focusing on developing hybrid architectures for deep learning-based image processing and methods for multimodal medical data. We will