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Field
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to develop deep learning models for analyzing whole-slide histopathology images, as well as natural language processing (NLP) methods for clinical records such as pathology reports and electronic health data
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We are seeking a highly motivated postdoctoral Research Fellow to join our interdisciplinary project on how cryogenic OrbiSIMS, a cutting-edge mass spectrometry imaging technique, interacts with
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people with subclinical and clinical disordered eating and related body image concerns and body image management efforts. Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not
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computing. With extensive experience in medical image analysis, computer vision, and AI systems through collaborations with leading institutions. Key Responsibilities: Conduct advanced research in the areas
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in image processing, quantitative analysis, and biological interpretation Proficiency in AI/machine learning tools for image segmentation, transformation, registration, or tracking Solid mathematical
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inspection, develop a database of raw and processed thermographic images of different defects - geometries on composites, test of thermographic image processing tools and techniques (all applicable
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of what microscopes can achieve. You will create and apply sophisticated algorithms, physics-based simulations, and machine learning models to process complex data from our cutting-edge imaging systems
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of what microscopes can achieve. You will create and apply sophisticated algorithms, physics-based simulations, and machine learning models to process complex data from our cutting-edge imaging systems
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Responsibilities: To independently undertake research on data-efficient object detection with key techniques in few-shot learning, transfer learning, image synthesis, etc. To produce research reports and/or
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during routine medical imaging. This reduces reliance on invasive biopsies and accelerates diagnostic processes. Beyond classification, the platform combines multimodal inputs to provide personalized