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Field
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) Interpretable machine learning for network adaptation. In this thesis, the student will study how interpretable models and explainable learning algorithms could be used in real cellular networks for safe
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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new
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data preprocessing, algorithm development, and optimization techniques. Excellent communication skills in English. Prior experience with environmental or photocatalytic systems is a plus. A fully funded
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. About your role: Develop improved physical models of the image formation process in holographic X-ray imaging Design and implement reconstruction algorithms for handling large-scale tomographic data from
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Are you passionate about developing intelligent algorithms that can support repair and remanufacturing decisions for sustainable manufacturing? As a PhD researcher, you will create innovative
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advance scholarship on who is most often exposed to algorithmic persuasion, how different types of people cope and are affected, and how we can develop remedies to reduce negative consequences. By doing so
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Researcher will influence the direction of application areas and algorithm development, receiving direct training in InSAR processing, geospatial data science, and agricultural remote sensing. Co-supervision
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The Computational Protein Engineering (CPE) group at The Novo Nordisk Foundation Centre for Biosustainability (DTU Biosustain) is developing novel methods to engineer proteins more effectively using
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involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can
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implementation of security provisioning algorithm in DFL environment Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research FieldEngineering » Communication