Job offer Postdoctoral Research Associate on Machine-Learning Assisted Simulation of non-Newton[...]

City University London; Camden Town; On-site; GBP 35,000 - 45,000; Full time

Job summary

A renowned educational institution in Camden Town is seeking a Postdoctoral Research Associate to work on the E-COOL project, developing innovative cooling technologies for electric motors. The role involves collaborating with academic and industrial partners and contributing to publications and presentations. Applicants should hold a PhD in relevant fields and have experience in computational research and machine learning tools.

Qualifications

• PhD or equivalent relevant qualification.
• Experience in constitutive models for fluid rheology.
• Capability of working in High Performance Computing Environments.

Responsibilities

• Conduct research to meet project aims and specifications.
• Collaborate with academic and industrial partners.
• Present findings in international journals and conferences.

Skills

Proven track record of peer-reviewed activity in research

Expertise in Machine Learning tools such as PyTorch or TensorFlow

Experience in computational research, rheology, and Non-Newtonian flows

Dissemination/Presentation skills

Education

PhD in Mechanical Engineering, Physics or relevant fields

Tools

CFD software (open-source or commercial)

High Performance Computing Environments

Job description

City, University of London, along with Otto von Guericke University Magdeburg (DE), Lund University (SE), National Technical University of Athens (GR), and industrial partners Lubrizol Ltd (UK) and AVL List Gmbh (AT), referred to as 'The Team', have been successful in their project proposal E-COOL, 'A Holistic Approach for Electric Motor Cooling', submitted to the European Innovation Council (EIC). The Team is looking to appoint one Postdoctoral Research Associate.

E-COOL aspires to develop a holistic e-motor cooling technology, maximising heat transfer through direct-contact, spray cooling. The project aims to achieve this technological breakthrough at time-scales compatible to those required for industrial innovation, in a bespoke manner integrating two interdisciplinary activities: (a) development and synthesis of novel oil-based, dilute polymer mixtures of non-Newtonian nature, which, when employed in spray-cooling systems, have the potential to be a disruptive technology; (b) implementation of a universal design methodology for such systems optimised with the aid of new Machine Learning (ML) algorithms.

Training datasets for the ML tool will be provided by accurate 'ground-truth' experimental and numerical investigations, also to be conducted for the first time in E-COOL. The envisioned cooling system aims to provide unprecedented cooling rates at local temperature hot spots, contributing to an average 20% increase in e-motor efficiency compared to today's state-of-the-art. This will allow next-generation e-motor utilisation over the whole range of transportation sectors, thus, facilitating significant additional energy and CO2 savings.

Main Responsibilities

• To undertake research in the field specified to meet the aims, objectives and project specifications given in the proposal funded to create this appointment.
• To liaise closely with the academic and industrial partners of the project.
• To play a leading role in raising the national and international profile of The Team by publishing articles in international journals, attending conferences, enhancing appropriate links with industry and Institutions and through other appropriate routes.
• To prepare and make presentations of the work to City and at major meetings and Conferences, as specified by the Principal Investigators and/or Head of Centre/or the Dean.
• To work as a member of the research centre within the discipline, playing a full part in the research life of the research team of the partner institutions.
• To play an appropriate role in planning and shaping future strategy and direction, working with other staff within The Team in a collegiate framework.
• To undertake other duties as required by the project coordinator.

Research

• To build upon the previous experience of the Team on the Computational Fluid Dynamics (CFD) simulation of non-Newtonian/viscoelastic fluids and to develop novel closures for fluid rheology using Tensor-Basis-Neural-Network (TBNN) architectures.
• To work with The Team, in order to develop reliable numerical methodologies capable of simulating the flow and thermal behaviour of complex heat-transfer liquids suitable for thermal management systems of electric motors.
• To collaborate with existing members of The Team, bringing together areas of expertise for innovative analytical and numerical research.
• To take an appropriate role in facilitating inter-disciplinary research links with other research centres and groups.

Requirements

• PhD or equivalent
• First degree and PhD in Mechanical Engineering, Physics or relevant fields
• Proven track record of peer-reviewed activity in research
• Experience in constitutive models for fluid rheology
• Experience in computational research in the field of the project, with a strong background in rheology and Non-Newtonian flows
• Expertise in Machine Learning tools such as PyTorch or TensorFlow
• Familiarisation with Artificial Neural Network architectures
• Code development and customisation of open-source or commercial CFD software
• Capability of working in High Performance Computing Environments
• Dissemination/Presentation skills and ability to present technical data in diverse audiences