🚨 PhD Scholarship Opportunity – Shape the Future of Offshore Wind Monitoring We are looking for a talented and driven PhD candidate to join the AURA CDT and the University of Sheffield’s Dynamics Research Group. This fully funded project is supported by leading industry partners: Ørsted, Siemens Digital Industries Software, and Guided Ultrasonics Ltd. (GUL). Project Title: Physics-Informed Digital Twin for Guided-Wave-Based Structural Health Monitoring (SHM) of Offshore Wind Turbine Foundations Offshore wind is growing faster than ever, and ensuring the reliability of turbine foundations is one of the biggest engineering challenges. This project gives you the chance to develop new technology that could redefine how the offshore wind industry maintains critical infrastructure. You will build a next-generation digital twin by integrating ultrasonic guided wave sensing, high-fidelity finite element modelling, and machine learning. Your work will contribute directly to real-time condition assessment and more efficient maintenance. Applicants must have a First-class or 2:1 degree in Engineering, Physics, Mathematics, or Computer Science. Experience in relevant fields is beneficial but not essential. Enthusiasm to learn is key. Applications are open until 5 January 2026. For full project details and application information, visit the provided link.

This EngD/PhD project at the University of Sheffield, sponsored by Ceratizit, focuses on developing advanced real-time temperature monitoring systems for high-speed CNC machining in the context of smart manufacturing and Industry 4.0. The project addresses the critical challenge of excessive heat generation during high-speed milling, which leads to rapid tool wear, poor workpiece quality, and dimensional inaccuracies. By integrating novel wireless sensor systems and intelligent predictive models, the research aims to enable autonomous machine tools to monitor and adjust cutting temperatures in real-time, thereby enhancing productivity, reducing tool damage, and minimizing scrap rates in advanced manufacturing sectors such as aerospace and automotive. The research involves a deep dive into digital engineering, combining powerful sensing technologies with predictive analytics. Key research questions include the implementation of non-intrusive, in-process wireless sensors in harsh milling environments, analyzing the relationship between high-frequency thermal events (using advanced Fibre Optic Infrared Radiation Thermometry) and tool wear/thermal stresses, and developing real-time predictive models to infer cutting temperatures in traditionally inaccessible areas. The successful candidate will be responsible for the full lifecycle of the smart system, from sensor design and calibration to integration, real-time data acquisition, and the creation and validation of predictive models using the AMRC’s 5G-enabled infrastructure. The project offers significant impact by improving process efficiency through predictive maintenance and intelligent automation, enhancing part quality by reducing temperature-induced defects, and supporting digitalization goals across precision engineering industries. Students will benefit from a fully funded four-year scholarship, including tuition fees, an annual tax-free stipend of £28,000, and a £35,000 research support grant for equipment, travel, and consumables. The program provides access to world-class facilities, tailored training, and support for gaining Chartered Engineer (CEng) status. Graduates have a strong track record of securing senior roles in top industries. Applicants should hold a first-class or strong 2:1 degree in a relevant engineering or science discipline (such as Electrical and Electronic Engineering, Mechanical Engineering, Aerospace Engineering, Materials Science, Physics, or Mechatronics and Systems Engineering). International and UK students are eligible, with specific residency requirements for UK Home status. English language proficiency (IELTS 6.5 overall, minimum 6.0 in each section) is required for non-native speakers. Applications are made via the University of Sheffield admissions system, selecting the 'Doctoral Training Course' and 'MADE4 Manufacturing CDT' options, and must include transcripts, degree certificates, CV, personal statement, and evidence of English proficiency if applicable. For further information, applicants can contact Dr Matthew Hobbs at [email protected].