Research Fellow in Digital Twining of Ceramic Coating

Key Skills

Effective communication skills, Machine learning techniques

Job Description

Are you experienced in computational modelling of complex flows for materials technology and ceramic coatings. Do you want to join a world class multidisciplinary team with industry partners. Are you looking for a new and exciting challenge to develop innovative digital tools combining CFD and machine learning to reduce manufacturing-induced deficiencies of ceramics.

We have a vacancy for an enthusiastic researcher with expertise in computational fluid dynamics (CFD), complex (non-Newtonian) flows, and machine learning knowledge to work with us in the Institute of Design, Robotics and Manufacturing (School of Mechanical Engineering, University of Leeds) and a local industry partner.

You will lead work on investigating and optimising the influence of compositional change, temperature, and humidity on the rheological behaviour of a ceramic slurry using CFD. Collaborating with other colleagues and the industrial partner, you will be defining a process window for the manufactured ceramic coatings by benchmarking surface quality, thickness, uniformity, and leakage, and subsequently developing machine learning algorithms to optimise the various parameters involved in the process. You will collaborate closely with other researchers in the to develop new learning and to disseminate the project findings via publications, and presentations.

As a Research Fellow you will have a PhD (or have submitted your thesis before taking up the role), and a Bachelors or Masters degree in Mechanical Engineering, Aerospace Engineering, Maths/Computer Science, Materials Engineering or a related discipline.

To explore the post further or for any queries you may have, please contact:

Dr Masoud Jabbari, Lecturer (Assistant Professor)

Email: M.Jabbari@leeds.ac.uk