Location: South Kensington, London
The Department of Chemical Engineering is seeking to hire a Research Associate to develop advanced methodologies based on hybrid machine learning-computational fluid dynamics (CFD) approaches for complex multiphase flows. The post is funded by the Engineering and Physical Sciences Research Council (EPSRC) as part of the multi-university Programme Grant PREMIERE. The flows of interest for the current project feature turbulence and interfacial topological transitions including breakup and coalescence; other features could include heat transfer and chemical reactions as well as complex geometrical effects. The methodologies will yield efficient and reliable solutions to these flow which will allow the creation of surrogate models with uncertainty quantification.
You will interact closely with the numerical modelling effort in the Matar Fluids Group. There will also be opportunities to collaborate with Imperial researchers working in the Department of Earth Science and Engineering, and the Data Science Institute within the PREMIERE programme at Imperial, as well as researchers in the PREMIERE partner institutions, Birmingham, Cambridge, and UCL. You will also interact closely with the multi-sector industrial collaborators of PREMIERE and the Matar Fluids Group and be expected to submit publications to refereed journals, help attract external research funding, and help Prof. Matar with co-supervision of members of the Matar Fluids Group.
Duties and responsibilities
You will develop models based on the use of a combination of machine learning and CFD approaches to solve multiphase flow problems. You will take initiatives in the planning of research and ensure the validity and reliability of the results/data generated, at all times. You will maintain accurate and complete records of all findings, write reports for submission to research sponsors, as appropriate, present findings to colleagues and at conferences, and submit publications to peer-reviewed journals. You will also provide guidance to staff and students in the Matar Fluids Group and the PREMIERE programme, attend Group meetings, and assist in the supervision of undergraduate and postgraduate research students and research assistants as required. You will also contribute to bids for research grants. You will comply with the College and Departmental safety practices and to attend courses on safety when appropriate and carry out other duties as may be deemed reasonable by Prof. Matar.
You should hold a PhD in Engineering, applied mathematics, or a related discipline, and a strong background in machine learning, CFD with parallelisation, applied mathematics, and multiphase fluid dynamics is essential; knowledge of and experience in uncertainty quantification, data-assimilation, as well as reactive multiphase flows with heat transfer is desirable. You should have considerable experience in developing models and numerical tools as well as knowledge of programming languages, e.g., python, C++, and open-source CFD software, e.g., OpenFOAM.
*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £40,694 to £43,888 per annum.
You can start immediately and the contract will run until 31 March 2025 in the first instance, with the possibility of extension subject to research funding. The post is based in the Department of Chemical Engineering at Imperial College London (South Kensington Campus).
*New appointments will have a maximum starting salary of £45,593 based on experience.
Informal enquiries about the post can be made to Professor Omar Matar ([email protected])
To apply, please visit our website: https://www.imperial.ac.uk/jobs/. You will find this vacancy by searching either the position title or job number: ENG02666. Candidates will need to complete an online application.
Further information about the post is available in the job description.
Should you have any queries about the application process please contact [email protected].
Closing date: Midnight on Sunday 10 September 2023
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