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learning models to distinguish between normal physiological behaviour (e.g. diurnal rhythms, feeding responses) and abnormal stress-induced patterns will be central to the project. This requires
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-making process. Research Objectives Model Learning in Dynamic Contexts Investigate the use of reinforcement learning for constructing and updating probabilistic world models (transition and observation
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
-latency, and scalable operation in aerial 6G networks. In this regard, Large Language Models (LLMs) have recently emerged as a key technology to achieve adaptive 6G spectrum management. The core idea of LLM
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to other indicators of unrest, such as seismicity. This PhD project will drive innovation in modelling magma-mush processes and the generated surface deformation and seismicity during unrest episodes
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The University of Exeter has a number of fully funded EPSRC (Engineering and Physical Sciences Research Council ) Doctoral Landscape Award (EPSRC DLA) studentships for 2026/27 entry. Students will be given sector-leading training and development with outstanding facilities and resources. The...
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and
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-making process. Research Objectives Model Learning in Dynamic Contexts Investigate the use of reinforcement learning for constructing and updating probabilistic world models (transition and observation
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space that the LLM can natively process. Second, a task-specific networking head will replace the LLM’s default language modelling head. This module will map the LLM's output features directly to a