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