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to continuously learn, adapt, and refine world models in self-adaptive and autonomous systems. Specifically, the research will investigate how AI-based methods can support the evolution and updating of transition
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to address urgent challenges in animal conservation and welfare. However, existing technologies have mostly been developed for use in controlled laboratory settings and are often unsuitable for field
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equitable access to clean energy. By linking technical innovation with legal reform and social inclusion, this project aims to develop a regionally tailored strategy for sustainable, just energy transition
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. The framework consists of three key technical innovations. First, a multimodal encoder will be developed to project heterogeneous network data (e.g., time-series, sequences, graphs) into a token-like embedding
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
: Algorithm Validation and Use Case Demonstration (Months 27–36): This WP will first develop an integrated hardware–software testbed to systematically validate the performance of proposed solutions under
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an important role in the efficient integration and management of solar energy in modern power systems. The studentship project aims to develop a novel PV forecasting model based on physics-informed neural
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Project details: Global Navigation Satellite Systems (GNSS) support a wide range of applications, from smartphone navigation to autonomous vehicles. The global GNSS market is substantial, with billions of consumer devices relying on accurate positioning daily. Improving GNSS accuracy, especially...