Antonio Attili
Top university
11 months ago
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PhD in Machine Learning for Turbulent Flows (CFD, Generative AI, Fluid Mechanics) University of Edinburgh in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
This is a competitive PhD scholarship. Several funding streams are available, including School of Engineering PhD Studentships and Scholarships, and EPSRC CDT in Machine Learning Systems studentships. Funding may cover tuition and provide a stipend, but specific amounts are not mentioned. Self-funded students with external scholarships are also welcome to apply.
Deadline
Expired
Country
United Kingdom
University
University of Edinburgh
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Keywords
About this position
The University of Edinburgh, in collaboration with Heriot-Watt University, is seeking a highly motivated PhD student to join a cutting-edge research project at the intersection of machine learning and turbulent flow modeling. The project focuses on developing and training advanced machine learning and generative AI architectures, such as Generative Adversarial Networks (GANs), to improve turbulence modeling in computational fluid dynamics (CFD). Traditional turbulence models like RANS and LES often lack the accuracy needed for engineering design due to their inability to resolve fine-scale physics. This project aims to overcome these limitations by leveraging high-fidelity DNS data and data-driven approaches to reconstruct small-scale turbulent structures, similar to super-resolution techniques in computer vision.
The successful candidate will work on designing high-accuracy turbulence closures using DNS datasets, pushing the frontier of ML-enhanced modeling for wall-bounded and shear-layer turbulence, and building a solid foundation in turbulence physics and modern data-driven methods. The research has broad applications, from aerodynamics to atmospheric flows, and is a critical step toward deploying ML-based models in real engineering scenarios.
The position is jointly based at the University of Edinburgh School of Engineering and the Institute of Mechanical, Process and Energy Engineering (IMPEE) at Heriot-Watt University. The project is supervised by Antonio Attili and Ali Ozel, both active researchers in fluid mechanics and machine learning. The TARFS Lab offers a vibrant research environment with opportunities to collaborate on fundamental and applied problems in fluid mechanics and simulation paradigms.
Funding is available through competitive PhD scholarships, including School of Engineering Studentships and EPSRC CDT in Machine Learning Systems studentships. Self-funded students with external scholarships are also encouraged to apply. The position is open only to UK candidates. Applicants should have a strong background in engineering, physics, applied mathematics, or a related field, and experience with machine learning, CFD, or turbulence modeling is highly desirable.
To apply, send your CV, transcript, and cover letter to Antonio Attili at [email protected]. For more information, visit the TARFS Lab website or the CDT in Machine Learning Systems page. Applications are reviewed on a rolling basis.
Funding details
This is a competitive PhD scholarship. Several funding streams are available, including School of Engineering PhD Studentships and Scholarships, and EPSRC CDT in Machine Learning Systems studentships. Funding may cover tuition and provide a stipend, but specific amounts are not mentioned. Self-funded students with external scholarships are also welcome to apply.
What's required
Applicants must be highly motivated and have a strong background in engineering, physics, applied mathematics, or a related field. Experience with machine learning, computational fluid dynamics (CFD), or turbulence modeling is highly desirable. The position is open only to UK candidates. Applicants should submit a CV, transcript, and cover letter. No specific GPA or language test requirements are mentioned.
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Professors
Lecturer - Assistant Professor
United Kingdom
Research Interests
Fluid Mechanics
Aerodynamics
Flame Dynamics
Supersonic Flow
Transonic Flow
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Prepare your application
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