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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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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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pollution, and overall, the causes of tree death. The understanding and methods developed as part of the project are expected to be useful for similar studies at different sites around the world, in order to
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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