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II is looking for a part-time (30 hours per week) PhD-Position: Machine Learning / Medical Imaging (m/f/x) (with immediate effect). This position is offered for a duration of 3 years. Join the AICARD
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using nano-vibration detection and super-resolution imaging. Funded by the Royal Society APEX Award (with support from the Royal Society, British Academy, Royal Academy of Engineering, and Leverhulme
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per week) PhD-Position: Machine Learning / Medical Imaging (m/f/x) (with immediate effect). This position is offered for a duration of 3 years. Join the AICARD Research Project “Transforming Cardiac
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single-molecule, microfluidics, super-resolution techniques, and chemical/synthetic biology tools. PhD project: High Resolution Fluorescence Imaging of Protein Plasticity We are now seeking a PhD student
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Early and accurate cancer detection remains a critical global healthcare challenge, with profound implications for patient outcomes and treatment strategies. While Time-of-Flight Positron Emission Tomography (ToF-PET) offers vital functional and molecular insights for improved cancer staging,...
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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How to apply: uom.link/pgr-apply-2425 This 3.5-year PhD project is fully funded for home students or EU students with settled status; the successful candidate will receive a tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to...
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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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