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Field
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Responsibilities Femtosecond laser spectroscopy of molecules and aggregates inside superfluid helium nanodroplets Implementation of a Timepix3 event-based imaging detector to enable 3D momentum reconstruction Time
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infrastructure managers in developing cross-sectoral strategies to proactively shape infrastructure demand, as an alternative to the traditional and increasingly untenable 'predict-and-provide' paradigm. By
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laboratory (wind-wave facility) experiments, using state-of-the-art imaging techniques developing computational codes to process and understand large experimental datasets (e. g., image processing) analyzing
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR imaging and spectroscopy
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synthesizing detectable targeting ligands for engineered immune cells and image and quantify the target cell engagement and efficiency. The project is highly multidisciplinary and involves aspects of synthetic
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lengthy processing times associated with sequencing. This PhD project aims to develop innovative artificial intelligence (AI) methodologies by integrating histopathology images and RNA sequencing data
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per week) PhD-Position: Machine Learning / Medical Imaging (m/f/x) (with immediate effect). This position is offered for a duration of 3 years. Join the AICARD Research Project “Transforming Cardiac
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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imaging technologies, such as cryo-electron tomography (cryo-ET) and expansion microscopy, have been pivotal in advancing our understanding of the structure and function of motile cilia. In contrast