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Field
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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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) 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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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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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning
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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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these technologies can only read DNA fragments of limited length. We enable biological interpretation of these sequencing data sets by developing algorithms based on graph theory, discrete optimization and machine
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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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while developing transferable skills in an international, interdisciplinary setting. The 15 open positions: DC1: Low-power IoT sensing at high frequency bands Host: IMDEA Networks Institute, Madrid, Spain
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that may empower consumers to resist persuasion and reduce harmful consequences? Are you passionate about advertising, algorithms and social media? Then apply for this PhD project focused on demystifying