Applications are invited for the above position with an immediate start to develop outstanding research in the area of engineering simulation and data science.
Manchester was the birthplace of the engineering discipline, and the Department of MACE has historical routes in the Institute of Mechanics in founded in 1824. Today the School hosts 1300 undergraduate and 450 postgraduate students, delivers undergraduate programmes across Mechanical, Aerospace and Civil Engineering, and MSc programmes in both technical engineering subjects and management. With 120 academic staff, 90 technical and support staff, and 300 postgraduate research students and post-docs, the Department is at the forefront of engineering education in the UK.
MACE has substantial world leading activity in the development of methods for advanced computational engineering, with a sizeable team of academics working on a range of related topics; turbulence, multiphase/multiphysics, particle methods, fluid structure interaction, biofluids and advanced materials. We seek a candidate with knowledge of one or more of these methods as well as significant experience in a complementary area of data science.
This position will help grow links between MACE and the Institute for Data Science and Artificial Intelligence, and collaborators on other departments across The Faculty of Science & Engineering. Applications from candidates able to demonstrate relevance of their research to The University’s themes of ‘Clean Growth’ and ‘Digital Engineering & Manufacturing’ are particularly welcomed. The post is also strategically aligned to the recently relaunched Modelling and Simulation Centre, which in collaboration with EDF is focussed on the delivery of ‘System-Level’ engineering simulation capabilities across a range of sectors.
You will undertake original research in engineering simulation and be able to demonstrate evidence of excellent prior work, as well as a clear workplan for developing their research in the future. In particular we are looking for candidates with demonstrable expertise in either of the following two themes:
1) Physics-informed Machine Learning for engineering simulation; i.e. the incorporation of AI/Machine Learning techniques to improve model accuracy and their range of applicability in engineering;
2) Data science techniques for engineering simulation; including methods derived from an applied mathematics background such as: uncertainty quantification, data assimilation, surrogate modelling, multi-objective optimisation.
As an early career academic, you will be given an appropriate level of teaching responsibility and will be expected to develop innovative approaches to improve the quality of teaching, learning and feedback to enhance the student experience.
As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
Blended working arrangements may be considered
Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.
Enquiries about the vacancy, shortlisting and interviews:
Name: Dr Alistair Revell.
Prof Tim Stallard.
This vacancy will close for applications at midnight on the closing date.
Please see the link below for the Further Particulars document which contains the person specification criteria.