E Kim

This fully funded PhD position at Coventry University, within the Centre for Fluid and Complex Systems, focuses on the intelligent modelling of self-organised edge turbulence in tokamak plasmas. The project is part of the UK Fusion Centre for Doctoral Training (Fusion CDT) and aims to advance predictive understanding of turbulence and instability dynamics at the edge of fusion devices. These edge phenomena are critical for plasma confinement and the prevention of disruptive events, yet are challenging to diagnose and model using traditional approaches. The research will blend physics-based modelling with modern artificial intelligence and machine learning techniques to develop new models for understanding and predicting complex plasma behaviour. The successful candidate will study plasma physics and turbulence, develop and apply computational models, and collaborate with a national training cohort and international partners such as NTU, the KSTAR team, and Deakin University. Applicants from backgrounds in physics, mathematics, engineering, or related fields are encouraged to apply. Full training is provided through the Fusion CDT, and no prior experience in all areas is required. The position offers comprehensive research training, including technical, personal, and professional development, as part of Coventry University's Doctoral and Researcher College. Funding: The position is fully funded, covering tuition fees and providing a bursary (stipend) for living expenses. Eligibility: Candidates must hold at least a 2:1 first degree in a relevant discipline, with a minimum 60% in the project element and overall module average. English language proficiency is required (IELTS academic overall minimum score of 6.5, with at least 6.0 in each component). Applicants should demonstrate the potential to engage in innovative research and complete the PhD within four years. Application Process: Applications must include full supporting documentation, a covering letter, and a 500-word supporting statement outlining the applicant’s expertise and interests relevant to the project. For further information, contact Prof E Kim at [email protected]. Deadline: 25 April 2026. For more details and to apply, visit the project page on FindAPhD.