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computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning
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This position is inside the SPETRA doctoral training unit which investigates new materials, methods and concepts for converting sunlight into usable energy sources. Inorganic Chalcogenide
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-anonymization, including researching current de-anonymization strategies Deploying and benchmarking anonymization methods. Investigation of state-of-the-art methods from cryptography and privacy and see their
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methods and in vitro screening approaches will be used to systematically elucidate the amyloidogenic potential of the gut metaproteome. Specifically, gene-catalogues and genome collections, along with
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methods for causal inference in observational data, is strongly preferred. Using various existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the
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Lorentzian manifolds through experimentation (Karin Melnick) - Representation-theoretic methods in algebraic geometry (Karin Melnick & Pieter Belmans) - Computational experiments with 3-dimensional hyperbolic
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transcripts A 3-page research proposal that presents your research project for the PhD: your research question, a short literature review, an overview over the methods and data that you aim to use The names
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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communications systems and methods for enhancing the physical layer security of next-generation wireless systems against threats such as jamming attacks. For details, you may refer to the following: https