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Number of awards: 2 Start dates: January, April or October 2026 About the project Two PhD studentships are available as part of the project ‘BIRES: Building body image resilience in populations
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AI design of ultrathin lenses for compact imaging devices The Faculty of Engineering at the University of Nottingham is seeking an enthusiastic, self-motivated student who enjoys working as part of
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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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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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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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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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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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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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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 challenging properties of uncertainty, irregularity and
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer