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environmental inputs, algae physiological parameters and microbial community eDNA data to develop predictive mechanistic models which can be utilised to develop an optimal cultivation strategy. The project is
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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resource-constrained environments, and it is important to investigate whether features derived from different network layers can be effectively combined. Machine Learning Model Development & Optimization
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the power of AI/ML and software-defined networking (SDN), and distributed learning methodologies, the research will focus on creating self-configuring, self-optimizing, and self-healing mechanisms for real
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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Start Date: October 1 2025 Introduction: This PhD project in Aero-Thermo-Structural Simulation and Optimization of Mechanical Interfaces in Hypersonic Vehicles will be carried out under the UK
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. Background and aims: As we move towards the future of large communication networks and remote sensing, applications such as 6G communications will require higher data rates, wider bandwidth, and stronger
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sleep patterns, gaining insights into sleep-related issues, and personalised sleep health management. However, the dynamic nature of sensor networks caused by frequently adding and removing nodes has
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. The studentship is fully funded for 3 years. The pan-University Graduate School offers an integrated training programme to the postgraduate community within a single centre, serving to inculcate interdisciplinary
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PhD Studentship available in the EPSRC Centre for Doctoral Training in Robotics and AI for Net Zero (RAINZ) This studentship is offered by the EPSRC Centre for Doctoral Training in Robotics and