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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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materials that combine the structural features and mobility in a single component. These are liquid crystal elastomers (LCEs), unique responsive materials that reversibly change shape, colour, adhesive
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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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associated threats. The research project of the PhD student will thus focus on defining methods to track, monitor, and manage the use of GenAI. While this can rely on recentely proposed telemetry framework
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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