City St George’s, University of London is the University of business, practice and the professions and brings together the expertise and excellence of City, University of London and St George’s, University of London into one institution.
The combined university is one of the largest suppliers of the health workforce in the capital, as well as one of the largest higher education destinations for London students.
Combining a breadth of disciplines across health, business, law, creativity, communications, science and technology, we are creating a ‘health powerhouse’ for students, researchers, the NHS and partners in uniting a world-leading specialist health university. We are now one of the UK’s largest health educators, where staff and students have access to an expanded team of brilliant academic and professional services colleagues, combined resources and facilities and more interdisciplinary opportunities.
The merger creates opportunities to generate significant change in the world of healthcare including changes to treatment, population health monitoring, workforce development and leadership, policy, and advocacy.
Background
City, University of London along with Otto von Guericke University Magdeburg, Lund University, National Technical University of Athens and industrial partners Lubrizol Ltd and AVL List Gmbh participate in the project E-COOL, ‘A Holistic Approach for Electric Motor Cooling’, funded by the European Innovation Council. E-COOL aspires to develop a holistic e-motor cooling technology, maximising heat transfer through direct-contact, spray cooling. The Team is looking to appoint one Postdoctoral Research Associate on Machine-Learning Assisted Simulation of non-Newtonian Flows.
Responsibilities
The Team aims to synthesise novel, non-Newtonian coolants to be employed in spray-cooling systems for e-motor stator windings. In order to achieve this, the Fellow will implement a universal design methodology for such fluids of complex rheology, using a Machine Learning (ML) algorithm to be incorporated in a Computational Fluid Dynamics framework. Training datasets for the ML tool, which will be based on a Tensorial Neural Network architecture, will be provided by Molecular Dynamics simulations also conducted in E-COOL.
Person Specification
The successful candidate will have a first-class degree and PhD in Mechanical Engineering, Physics or relevant fields. They will have experience in computational research in the field of the project, with a strong background in rheology and Non-Newtonian flows. In addition, they will be familiar with Machine-Learning tools (such as PyTorch or TensorFlow), as well as with code development and customisation. They should be able to showcase a proven track record of peer-reviewed activity in research.
Additional Information
The selection process will involve an interview and a presentation. Further details will be confirmed at the interview stage.
To apply and for more information about the post please use the links below.
City St George’s offers a sector-leading salary, pension scheme and benefits including a comprehensive package of staff training and development.
City St George’s, University of London is committed to promoting equality, diversity and inclusion in all its activities, processes, and culture for our whole community, including staff, students and visitors.
We welcome applications regardless of age, caring responsibilities, disability, gender identity, gender reassignment, marital status, nationality, pregnancy, race and ethnic origin, religion and belief, sex, sexual orientation and socio-economic background.
City St George’s operates a guaranteed interview scheme for disabled applicants.
The University of business, practice and the professions.
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