40 web-programmer-developer-university-of-liverpool PhD positions at University of Exeter
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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 train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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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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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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. 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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This project is a fully funded MSc by Research Scholarship in Medicinal Chemistry / Synthetic Organic Chemistry and builds on a successful decade-long research programme at the University of Exeter
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enquiries to: Johan Wahlstrom (j.wahlstrom@exeter.ac.uk) Please ensure you read the entry requirements for the potential programme you are applying for. To Apply for this project please click on the following
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The University of Exeter’s Department of Engineering is inviting applications for a PhD studentship co-funded by the partner Hydro International and University of Exeter Faculty of Environment
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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