3,044 image-processing "Integreat Norwegian Centre for Knowledge driven Machine Learning" PhD positions
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Chair of Biological Imaging 30.01.2026, Academic staff and positions for doctoral candidates We are now looking for a researcher (m/f/x) with an engineering or physics background and a motivation to
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build the sustainable companies and societies of the future. The research subject Process Metallurgy at the Department of Civil, Environmental and Natural Resources Engineering is looking for two doctoral
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computer vision method for medical image analysis principally in oncology or pathology, with a possible focus on generative AI, explainability, multi-modality. Candidate’s profile A good Bachelors degree
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project. The project will also employ a PhD student at Lund University, focusing on developing hybrid architectures for deep learning-based image processing and methods for multimodal medical data. We will
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heritage, materials science, and physics. Supported by 19 countries, the ESRF is an equal opportunity employer and encourages diversity. Context & Job description Thesis subject: Sub Nanosecond Imaging
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are seeking a Ph.D. student to join our multidisciplinary team developing a radical solution for better detection and treatment that uses ultra-thin snake-like robots and advanced optical imaging techniques
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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focus on quantum channel discrimination for high-resolution spectroscopy and AC field sensing. The project aims to develop theoretical frameworks for distinguishing closely related quantum processes
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a set confidence level, typically 90% probability of detection. This project aims to take a radically different approach and integrate the array images with structural integrity assessment to predict
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for imaging blood flow dynamics in cancer using nuclear imaging techniques. This position involves pursuing a PhD thesis under the supervision of Dr. Pellico as part of the project "NanoFLOW” funded by