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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan, under the supervision of Dr. Amin Paykani at Queen Mary University of London, as part of the Royal Society...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan, under the supervision of Dr. Amin Paykani at Queen Mary University of London, as part of the Royal Society...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan, under the supervision of Dr. Amin Paykani at Queen Mary University of London, as part of the Royal Society...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is a collaboration between Queen Mary University of London and Kyushu University, Japan, under the supervision of Dr. Amin Paykani.Key ResponsibilitiesDevelop and apply...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is a collaboration between Queen Mary University of London and Kyushu University, Japan, under the supervision of Dr. Amin Paykani.Key ResponsibilitiesDevelop and apply...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is a collaboration between Queen Mary University of London and Kyushu University, Japan, under the supervision of Dr. Amin Paykani, to realize the objectives of the...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan, under the supervision of Dr. Amin Paykani at Queen Mary University of London, as part of the Royal Society...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan, under the supervision of Dr. Amin Paykani at Queen Mary University of London, as part of the Royal Society...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is a collaboration between Queen Mary University of London and Kyushu University, Japan, under the supervision of Dr. Amin Paykani, to realize the objectives of the...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting the ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan, under the supervision of Dr. Amin Paykani at Queen Mary University of London, to realize the objectives of the...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting the ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan, under the supervision of Dr Amin Paykani at Queen Mary University of London to realise the objectives of the Royal...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe project aims to develop a reduced-order surrogate model for predicting ammonia direct injection spray characteristics using a hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan, under the supervision of Dr. Amin Paykani at Queen Mary University of London to realise the objectives of the Royal...
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Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the RoleThe goal of this project is to develop a reduced-order model for predicting the spray characteristics of ammonia direct injection using a hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan, under the supervision of Dr. Amin Paykani at Queen Mary University of London, to achieve the objectives of the...
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Data-Driven Modeling of Reactive Flows
2 months ago
Shape the Future of Combustion with Machine Learning
We are seeking a highly motivated Postdoctoral Research Assistant to contribute to a groundbreaking project at the forefront of machine learning and turbulent combustion modeling. This exciting opportunity will see you develop innovative reduced-order surrogate models for predicting ammonia spray characteristics, leveraging cutting-edge hybrid machine learning approaches.
Your Role:
- Develop advanced reduced-order surrogate models to predict key ammonia spray characteristics using a sophisticated hybrid machine learning approach.
- Collaborate closely with esteemed researchers at Kyushu University and Queen Mary University of London, fostering a dynamic and collaborative research environment.
- Contribute directly to a prestigious Royal Society International Science Partnership Funded project, pushing the boundaries of scientific discovery in the field of combustion modeling.
Your Expertise:
- Hold a PhD in Mechanical/Aerospace Engineering, Applied Mathematics, or a closely related discipline.
- Possess a strong foundation in modeling and simulation of multiphase turbulent reacting flows, demonstrating a deep understanding of complex combustion phenomena.
- Demonstrate expertise in applying machine learning tools to chemical kinetics and turbulent combustion problems, showcasing your ability to bridge the gap between theoretical knowledge and practical applications.
Join Our World-Renowned Institution:
Become part of the vibrant School of Engineering and Materials Science at Queen Mary University of London. Contribute to the Centre for Intelligent Transport, a leading hub dedicated to advancing future transport and mobility technologies.
Benefits:
- Enjoy 30 days' leave per annum, providing ample time for personal pursuits and well-being.
- Benefit from access to a comprehensive pension scheme, ensuring your financial security for the future.
- Receive competitive salaries and access to valuable personal development opportunities, fostering your professional growth and advancement.
