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, the role holder will be responsible for the supervision of PhD and MSc students working on the relevant topics with the project. It is an excellent opportunity for a researcher to further their skill set in
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potential applications in audio and music processing. Standard neural network training practices largely follow an open-loop paradigm, where the evolving state of the model typically does not influence
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on autofluorescence (AF) imaging and Raman spectroscopy for detection of metastatic lymph nodes during breast cancer surgery. Engaging with and reporting to Dr Alexey A. Koloydenko (Department of
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Population and Data Science. The Institute was ranked the number one place in the world to study ophthalmology (CWUR 2017 world rankings), attracting principal investigators, post-doctoral fellows and PhD
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-oncology and early detection research. Your position will be embedded in the CCC’s Advanced Microscopy Development Group, led by Dr Simon Poland and based in the purpose-built laser and microscopy
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experience in image data processing and analysis Familiarity with femtosecond/picosecond lasers and safe alignment practice. Clear, timely communicator who enjoys collaborating across physics, engineering and
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for conditions such as otosclerosis. The position requires expertise in medical image analysis, proficiency with neural network architectures (particularly CNNs for segmentation tasks), and experience processing
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of the tissue microstructure. In this position, you will work on developing advanced preclinical MRI techniques and you will employ them for 1) in-vivo mouse liver imaging, both for normal controls and in models
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regarded. Dissemination of findings through publication is an essential aspect of these positions. The selected candidate will collaborate as part of a cohesive unit to: Design, develop, and experimentally
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of the tissue microstructure. In this position, you will work on developing advanced preclinical MRI techniques and you will employ them for 1) in-vivo mouse liver imaging, both for normal controls and in models