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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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represents one of the four departments in the school (Community Health and Social Medicine, Medical Education, and Clinical Medicine). The department plays a critical role in our mission-driven BS/MD program
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the development of system software. Key questions include how LLMs can support programmers in writing complex logical code, generating high-quality tests, and optimizing performance. Moreover, when integrated with
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reach the limit of the electrical grid connections to their sites if the transition is not done in an optimal way, an issue that will be prominent in industrial, commercial and residential areas across
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intelligent systems aim to optimize power usage without compromising performance, employing strategies like power-aware computing and thermal-aware optimization. These systems are crucial in extending
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paid. We expect the stipend to increase each year. Only Home students are eligible for funding. The start date is October 2026. The project aims to develop and optimize metal oxide aerogel materials
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model predictive control (MPC) methods to enable large groups of buildings to dynamically form coalitions and provide flexible energy services. Your work will incorporate advanced robust MPC techniques
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simulations to analyze and optimize hemodynamic parameters in CAD-related applications. Collaborate with clinicians to translate simulation insights into practical solutions for cardiovascular health. Develop
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will have the opportunity to work closely with researchers on engineering design and management in the Exeter Digital Enterprise Systems (ExDES) lab and Queen’s University Belfast to Explore novel
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signals with trading intuition and live market insights to optimize existing strategies or build new ones from the ground up · Risk Management. Oversee the deployment and real-time operation of trading