Zoomposium 29: 31 August 2022

Watch Zoomposium29 (Passcode: %WU93ffR)

Dr Oana Dobre, James Watt School of Engineering

‘3D protein-based hydrogels for tissue regeneration applications’

I am a Research Associate in the Department of Biomedical Engineering at Glasgow University (2017-present). My research interests include the development of 3D in vitro culture systems such as hydrogels/bioinks to efficiently present growth factors in a controlled manner in order to promote tissue regeneration such as bone and muscle. I would like to collaborate with researchers interested in 3D cell culture systems, additive manufacturing techniques and electroactive polymers for their used in tissue regeneration. I am planning to apply for EPSRC/BBSRC grants to develop a bioactive polymer platform for musculoskeletal disorders.


Dr Wenjuan Song, James Watt School of Engineering

‘Superconducting Technology for Green and Efficient Transportation System and Electric Network’

My research features advancing the green aviation and other transportation systems, and efficient power systems by superconducting technology and artificial intelligence. My research topics are as follows: protection solutions, transmission lines for electrically powered aircraft system, R&D of high efficiency and low loss superconducting devices, Artificial Intelligence for superconducting components in modern cryo-electric aircraft. I am keen to collaborate with colleagues, researchers and industrial collaborators from aviation sector, power & energy sector, and health care sector, via publication, co-supervision, grant writing, and projects, etc.


Dr Eky Febrianto, James Watt School of Engineering

‘Physics-informed digital twin for resilient infrastructures’

A digital twin is a virtual model that accurately reflects the state of a physical object. Digital twin of infrastructures enables continuous monitoring and performance assessment throughout their life span. The copious amount of collected sensor data can be better understood through the aid of numerical prediction based on the law of physics. This talk will introduce a novel technique that synthesises measurement data and physics through Bayesian statistics and machine learning algorithms to predict the structural response of infrastructures. This talk will also present a case study of a nonlinear continuous welded rail (CWR) system under the danger of buckling due to extreme heat.


First published: 5 August 2022