Sort by
Refine Your Search
-
dynamics and, if appropriate, field work (Nicholas, Aalto); numerical modelling (Nicholas, Hawker); machine learning (Hawker, Aalto); and analysis of remote sensing (Aalto) and population datasets (Hawker
-
Productivity Index (RPI) using observed versus potential productivity modelled with machine learning (https://doi.org/10.1016/j.ecolind.2025.113208 ), this applied geospatial ecology project will study how
-
analyse the behaviour of individual animals under natural conditions are therefore vital not only for fundamental research to understand why animals behave in the ways they do, but also for applied work
-
, or applying consistent correction models across inter- and intra-satellite interactions. The use of multi-objective optimisation will enable systematic exploration of trade-offs between different classes
-
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
-
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
-
are fundamentally limited by a "one model for one task" design philosophy. This approach incurs prohibitive engineering costs and yields brittle solutions with poor generalisation to new network conditions, trapping
-
-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
-
storm to use these technologies and/or visit the affected area to evaluate storm-related tree damage. Therefore, to support sales planning and the safety of foresters working in the field, there is a need
-
-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