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Your Job: The PhD project is methodologically independent and embedded in a multidisciplinary research environment at the interface of artificial intelligence, scientific imaging, and materials
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Eligibility criteria - physics with knowledge in optics - knowledge in microscopy - knowledge in single-molecule imaging (acquisition and processing) - knowledge in photophysics - knowledge in cell culture
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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
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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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elucidating the molecular and cellular mechanisms of the late phase of long-term potentiation (LTP), a key process in learning and memory. The project is based on the development and use of an innovative
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of electron microscopy and beam-material interactions. The ideal candidate will have a basic understanding of image processing. Core competencies: – Knowledge of image processing software (Avizo, Dragonfly, etc
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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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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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-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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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