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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
Qualifications PhD in machine learning, computer vision or a related field. Established expertise in deep learning methods applied to images analysis. Experiences with generative models, volumetric image
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humans in playing board and computer games, driving cars, recognizing images, reading and comprehension. It is probably fair to say that an artificial neural network can perform better than a human in any
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program exploring the role of fluctuations in molecular transport processes by studying highly controlled experimental models at the meso- and nanoscale, funded by a UKRI Frontier Research Guarantee Grant
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histology or imaging techniques. ⦁ Familiarity with R or other statistical tools for data analysis. The postdoctoral researcher and PhD-student positions are initially limited to 2 and 4 years
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, primarily functional Magnetic Resonance Imaging (fMRI), combined with behavioural, cognitive, and affective measures. The successful candidate will join an interdisciplinary research team working
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We invite applications for a Postdoctoral Research Associate position at the University of Cambridge to join the Cambridge Image Analysis group in the Department of Applied Mathematics and
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the scope of BlueMat, taking advantage of the collaborative network within the excellence cluster Your profile PhD in physics or in a related discipline experience in phase contrast or coherent X-ray imaging
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Anaerobic Digestion (AD), brewing, distilling and dairy processing, are operated sub-optimally and as such are experiencing loses in productivity, yields and profits. These loses could be alleviated if key
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are recruiting a Post-Doctoral W/M whose work will focus on biological chemistry for the synthesis and imaging of non-natural sugars. The aim of the GlycoMechano project is to develop molecular tools capable
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vision transformer or large vision AI model Expertise in high performance computing Expertise in image and spatiotemporal data processing Expertise in federated learning on large computing clusters A