"Postdoctoral Research Assistant in "Data-driven Models for Reactive Flows"

Opportunity ID  

2505

Faculty  

Science & Engineering

Location  

QM London

Contract Type  

Fixed Term

Duration  

30 months

Working Patterns  

Full Time

Full Time Equivalent (FTE)

1.00

Working Hours (Per Week)  

35.00

Salary Range  

Grade 4/5 Research, £40,223 - £47,178

Job Pack  

Oleeo 2505 - PDRA Data-driven Models for Reactive Flow JP.pdf.pdf – 610KB 

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Contact Details  

Dr Amin Paykani at a.paykani@qmul.ac.uk

About the Role  

About the Role

The overall objective of the project is to develop a reduced-order surrogate model for predicting the ammonia direct injection spray characteristics using 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 Society International Science Partnership Funded project “Data-Driven Surrogate Modelling for Liquid Ammonia Direct Injection Spray Characteristics”. 

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.

The School is part of the Faculty of Science and Engineering, which comprises of five schools and two institutes. The Faculty’s student population has grown, both within the UK and overseas, in recent years and its research profile has strengthened considerably. The Faculty is focused on establishing itself as one of the leading Faculties for Science and Engineering in the UK. This position will be based in the Centre for Intelligent Transport (CIT) which combines complementary strengths in Mechanical and Aeronautical Engineering, Power Systems, Robotics and AI, Digital Design and Manufacturing, and Advanced Materials to drive future transport and mobility technologies that better our world.

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.

Our reformer heritage informs our conviction that great ideas can and should come from anywhere. It’s an approach that has brought results across the globe, from the communities of east London to the favelas of Rio de Janeiro.

We continue to embrace diversity of thought and opinion in everything we do, in the belief that when views collide, disciplines interact, and perspectives intersect, truly original thought takes form.

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.

Closing Date  

15/08/2024, 23:55 

Provisional Interview Date  

16 September 2024