PhD Studentship: Real time 4D Imaging System for Turbulent Multiphase Flows
Develop a groundbreaking real-time 4D imaging and AI/ML system to analyze complex turbulent multiphase flows.
Best for:Best for engineering or science graduates with a solid background in fluid mechanics and basic programming skills looking to explore experimental fluid dynamics and AI.
Overview
The University of Manchester offers a 3.5-year PhD studentship within the Department of Mechanical and Aerospace Engineering to pioneer a breakthrough real-time 4D imaging system. This project combines advanced event-based cameras with cutting-edge artificial intelligence and machine learning algorithms to capture transient multiphase flows like bubbly flows and liquid jet breakups. Working alongside Imperial College London and a multidisciplinary team, the successful candidate will generate critical benchmark data to improve industrial and biomedical computational models. The student is expected to design experimental setups, implement AI models, and collaborate effectively across academic institutions.
What you'll do
- Develop a real-time tomographic 4D imaging system utilizing multiple event-based cameras
- Design and implement AI and machine learning algorithms to predict flow transitions in real time
- Conduct experiments on benchmark transient multiphase flows, including bubbly flows and turbulent liquid jet breakups
- Analyze bubble-turbulence interactions, collisions, and coalescence dynamics to generate unique scientific insights
- Collaborate closely with research teams at Imperial College London and other partner institutions
- Contribute to the advancement of computational models for various industrial and medical applications
Requirements
Degree required: BSc (2.1 honours) or Master's degree in a relevant science or engineering discipline
Degree offered: PhD
Language: English
Instruction: English
- Strong theoretical and practical foundation in fluid mechanics
- Basic programming skills in any language
- Strong written and verbal communication skills
- UK citizens or residents meeting home fee criteria
Funding & terms
How to apply
Deadline: 2027-01-08 · open until filled
Interested applicants should contact the main supervisor, Dr Kuppuraj Rajamanickam, via email with their application materials. Submissions must include details of the applicant's current level of study, academic background, and relevant experience. Candidates are also required to include a dedicated paragraph detailing their motivation for pursuing this specific PhD project.
- Academic transcripts
- CV
- Motivation statement
Contact
Kuppuraj Rajamanickam· Main Supervisor
Create a free profile to unlock your personal match score, the full requirements, funding details and how to apply — in English or Persian.