Postdoctoral Research Assistant in Data-driven Models for Reactive Flows
2 weeks ago
About the Role
The 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 Royal Society International Science Partnership Funded project.
About You
The successful applicant will have, or soon obtain, a PhD degree in mechanical/aerospace engineering, applied mathematics, chemical engineering, or a related field. We are looking for candidates with a strong background in modeling and simulation of three-dimensional multiphase turbulent reacting flows and development and application of machine learning tools in chemical kinetics and turbulent combustion.
About the School/Department/Institute/Project
This post is within the School of Engineering and Materials Science, a large School with 70-80 academics and a similar number of postdoctoral research staff. There are around 1000 undergraduate and taught postgraduate students and 220 PhD students. These are supported by an administrative and technical staff team of 45. The staff and student body are international in make-up.
About Queen Mary
At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable. Throughout our history, we've fostered social justice and improved lives through academic excellence. And we continue to live and breathe this spirit today, not because it's simply 'the right thing to do' but for what it helps us achieve and the intellectual brilliance it delivers.
Benefits
In return, we offer 30 days' leave per annum, access to a pension scheme, a season ticket loan scheme, and competitive salaries. We also offer enhanced family-friendly leave. You will also work with a friendly team, with personal development opportunities. Queen Mary's commitment to our diverse and inclusive community is embedded in our appointments processes. Reasonable adjustments will be made at each stage of the recruitment process for any candidate with a disability.
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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, 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 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...
-
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...
-
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...
-
Canary Wharf, Greater London, United Kingdom Queen Mary University of London Full timeAbout the ProjectThe 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...
-
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 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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