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Shifting the paradigm: machine-assisted scholarly digital editing Digital Humanities Institute PhD Research Project Self Funded Dr Isabella Magni Application Deadline: Applications accepted all year
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. Certifications: --- Preferred Qualifications Education: PhD or doctoral degree in a related field. Specialized training or certifications in gynecological and obstetric imaging modalities (e.g., advanced
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-positions-application/list-of-phds APPLICATION Deadline: 15th Apr, 2026 Application open: 5th Jan, 2026 Eligibility criteria and application procedure: https://www.eu4greenfielddata.eu/phd-positions
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . To develop novel inertial microfluidics and impedance-based cell sorter. To develop novel label-free CQAs based
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into operational decision‑support tools for farmers, in close collaboration with an industry partner. The project focuses on automated rumen‑fill assessment using 3D imaging, computer vision, and predictive
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Supervisors: Prof Ioan Notingher (School of Physics and Astronomy) Dr George Gordon and Dr Abdelkhalick Mohammad (Faculty of Engineering) Funding: fully-funded (stipend and PhD fees) Start date
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(e.g., tensor or multi-modality data). Work with other researchers and PhDs to develop AI applications related to image processing, computational imaging as well as computer vision. Lead a team of junior
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cutting-edge research in areas such as pattern recognition, automation science, complex systems, AI for Science, robotics, machine learning, computer vision, natural language processing, biometrics, medical
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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Dortmund, we invite applications for the Research Group Analysis of Microscopic BIOMedical Images (AMBIOM): PhD Candidate (m/f/d) The position is part of the DFG-funded project: ComplexEye - a 96/384-well