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science/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
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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vision imaging technologies and machine learning methods to estimate physiological parameters (eat-readiness, shelf-life) of fruits at industrial sorting speeds. The work will include creating an accurate
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Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
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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
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VisionMetric Ltd (Vision Metric - Fast, Accurate, Cost Effective) to start in October 2026. CCTV footage is a predominant and crucial source of evidence in policing, with an estimated 21 million cameras
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DTU Tenure Track Researcher on Nanoreactors for Operando Visualizations of Nanoparticle Catalysis...
can read more about Center for Visualizing Catalytic Processes (VISION) at https://vision.dtu.dk . If you are applying from abroad, you may find useful information on working in Denmark and at DTU
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Grenoble, France. Through its innovative engineering, pioneering scientific vision and a strong commitment from its 700 staff members, the ESRF is recognised as one of the top research facilities worldwide
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enhancing low-quality facial images. However, there is little scientific evidence on whether these tools improve human accuracy, how they affect machine recognition, and whether they introduce demographic
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on language-agnostic modalities, including images and speech, as bridges to enhance cross-lingual transfer between high- and low-resource languages. The project combines resource creation with methodological