Cranfield University PhD Studentship: Cranfield University, in collaboration with Semtronics UK and international academic partners, offers a PhD studentship to enhance offshore wind turbine efficiency through advanced digital twin and machine learning technologies. The project aims to address critical knowledge gaps in integrating digital twins with machine learning, improving performance, decision-making, and reducing costs in wind energy optimization.
Cranfield University PhD Studentship: Enhancing Offshore Wind Turbine Efficiency through Digital Twin and Machine Learning Technologies
- 💡 Cranfield University, in collaboration with Semtronics UK and international academic partners, offers a PhD studentship focused on enhancing offshore wind turbines’ efficiency and reliability through digital twin and machine learning technologies.
- 🎓 The project aims to address critical knowledge gaps in integrating digital twin technology with advanced machine learning for wind energy optimization.
- 🌬️ Key challenges include limited research in comprehensive digital twins for wind farms, insufficient depth in advanced machine learning algorithms like deep learning for predictive maintenance, and gaps in real-time data processing methods for operational adjustments.
- 📊 The project will develop a holistic digital twin model incorporating various wind farm operation data, prioritize real-time data processing, and use synthetic and real sensor data for robust model validation.
- 💰 Successful applicants will receive a tax-free bursary of up to £20,000 plus fees for three years, with eligibility limited to UK students.
- 📅 Application deadline is 19th June 2024, with the start date on 30th September 2024, and the award duration is three years.
- 🎓 Applicants should hold a 1st or 2.1 UK degree or equivalent in disciplines related to electrical engineering, energy, or computer science, with strong programming experiences for wind turbines.
- 📝 To apply, eligible candidates should complete the online application form and contact for further information.
- 🌐 Cranfield University promotes diversity and inclusion, welcoming students and staff from all backgrounds, and supports initiatives for gender diversity in STEM fields and disability inclusion.
Summary Table:
- Application Deadline: 19 Jun 2024
- Award Type: PhD
- Start Date: 30 Sep 2024
- Duration: 3 years
- Eligibility: UK
- Reference Number: SATM465
Study Area: Electrical Engineering, Energy, Computer Science
Location: Cranfield University, United Kingdom
Eligibility/Qualification: Applicants should hold a 1st or 2.1 UK degree or equivalent in electrical engineering, energy, or computer science. Strong programming skills for wind turbines are required. Candidates should be self-motivated, possess excellent communication skills, and demonstrate an aptitude for industrial research.
Description: This PhD project focuses on integrating digital twin technology with advanced machine learning to optimize wind energy. It seeks to develop a comprehensive digital twin model using diverse data sources for enhanced predictive maintenance and real-time operational adjustments. The project will explore the scalability and adaptability of these models across different wind farm conditions, addressing key challenges in wind turbine performance.
How to Apply: For further information, contact Dr. Ravi Pandit via email at ravi.pandit@cranfield.ac.uk. Eligible applicants should complete the online application form.
Last Date: 19 June 2024
This scholarship offers an opportunity to contribute to cutting-edge research in wind energy optimization while benefiting from Cranfield’s dynamic research environment and professional development opportunities. Cranfield University values diversity and inclusion, fostering a culture where all students and staff can thrive.
