• Description:

    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.

    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.

    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.

  • Fields

    • Computer Science

    • Engineering

  • Qualifications

    • Master

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