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vision and deep learning methods relevant to imaging, together with programming experience using modern scientific computing tools such as Python and PyTorch or TensorFlow. The successful candidate will
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Location: The Francis Crick Institute, London Short summary We are seeking an ambitious Postdoctoral Fellow to develop the next generation of deep mechanistic models (DMMs; Fabrini & Fröhlich
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learning techniques pertaining to computational electromagnetics. If the successful candidate has not yet been awarded their PhD, appointment will be made as a Research Assistant (Grade 6B). Payment at Grade
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profiling and quality control. Develop and deploy deep learning models to support imaging-guided experimental decisions within the VISIBLE platform. Integrate imaging data with functional, screening and multi
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, biomedical engineering, medical imaging, or related field. Experience in deep learning with practical implementation. Strong Python skills and relevant frameworks. Experience with large clinical imaging
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systems development in Python, PyTorch and deep learning would be desirable. The post will also involve full-stack software development to turn research models into usable tools and demonstrators, so
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, biomedical engineering, medical imaging, or related field Experience in deep learning with practical implementation Strong Python skills and relevant frameworks Experience with large clinical imaging datasets
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to people and ecosystems Climate extremes and water hazards Hydrological modelling and forecasting Hydrologic deep learning Groundwater and surface-water interactions In addition, we welcome applications from
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spatial characteristics of TMEs [1]. In lung cancer, several deep learning studies using Haematoxylin and Eosin (H&E) images have demonstrated that the spatial organisation of stromal and immune cell
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We are focused on fostering education and training in research to develop microenvironments to investigate and instruct cellular behaviour including, but not solely, stem cell differentiation. Our