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–2 years, total 3–4 years) on deep learning for medical imaging. This DFG-funded project focuses on developing deep learning methods for medical and scientific imaging. The Professorship for Machine
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are seeking a Ph.D. student to join our multidisciplinary team developing a radical solution for better detection and treatment that uses ultra-thin snake-like robots and advanced optical imaging techniques
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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
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Scheme funded by the AHRC. Project Description The project focuses on Take One Picture (TOP) – a national programme for primary schools launched in 1995 by the National Gallery (NG) – to develop a new
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Novel optics and AI approaches to image the centre of a live root for the first time. This exciting opportunity is based within the thriving Optics and Photonics Research Group in Faculty
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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This PhD position is part of the ErUM-Data project CmarT, which aims to develop a novel multi-scale imaging approach at synchrotron radiation facilities, particularly PETRA III at DESY. Biological
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HBIGS Heidelberg Biosciences International Graduate School | Heidelberg, Baden W rttemberg | Germany | 3 days ago
Framework Programme? Other EU programme Reference Number DC12 Is the Job related to staff position within a Research Infrastructure? Yes Offer Description 15 PhD positions in imaging of Nuclear architecture
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Roentgen’s Nobel Prize-winning discovery of X-rays enabled us to non-destructively image inside the body, birthing medical diagnostic imaging and revolutionising materials characterisation
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Conventional x-ray imaging is firmly established as an invaluable tool in medicine, security, research and manufacturing. However, conventional methods extract only a fraction of the sample