Dr Laibing Jia

Personal statement

My academic journey began at the University of Science and Technology of China (USTC), where I earned my PhD, BSc, and BEng degrees, specialising in Fluid Mechanics. My PhD thesis on fluid-structure interaction (FSI) was recognised and published by Springer in their "Springer Theses: Recognizing Outstanding Ph.D. Research" series. This foundational work laid the groundwork for my subsequent research endeavours.

Following my PhD, I engaged in postdoctoral research in both China and the United States, with a focus on bio-inspired propulsion and renewable energy. These experiences enriched my understanding and broadened my expertise. Recognising my research potential, I was awarded the Royal Society K.C. Wong Postdoctoral Fellowship under the Newton International Fellowship scheme. I worked as a Royal Society sponsored Research Fellow at the University of Strathclyde. Following a period back to China, I returned to Strathclyde as a Lecturer and Chancellor's Fellow since 2019 under the Strathclyde Global Talent Programme.

My research is in the field of Fluid Mechanics, employing both experimental methodologies and computational fluid dynamics (CFD) to develop models that enhance our understanding of these complex interactions. My research interests includes,

1. Bio-inspired Propulsions: Inspired by natural phenomena such as fish swimming and bird flight, I lead projects focusing on bio-inspired propulsion systems and their underlying mechanisms. This research holds significant promise for developing sustainable technologies inspired by nature’s time-tested designs. These innovations can lead to advancements in underwater robotics and renewable energy systems, aligning with global sustainability goals.

2. Machine Learning with Digital Twins: The intersection between fluid mechanics and artificial intelligence is a rapidly advancing area. By leveraging machine learning-accelerated CFD algorithms and the digital twin concept, my work connects experimental fluid mechanics and CFD to estimate real-time, predictive models of physical systems. Utilising machine learning algorithms with sensor fusion, I aim to develop digital twins that can revolutionise marine operations, environmental monitoring, and educational methodologies.

Looking forward, I plan to evolve my research by integrating fluid mechanics with emerging fields of artificial intelligence (AI). Recognising the transformative potential of these fields, I am co-founding two Strathclyde Centres for Doctoral Training (SCDTs). One centre aspires to leverage AI to augment ocean forecasts for marine operations, while the other aims to utilise human-centred AI technologies to foster inclusivity in education and society. A crucial aspect of these initiatives is incorporating 'digital twins' into real-world applications. I am convinced that the confluence of digital twins, AI, and sensor fusion technologies can revolutionise our approach to critical applications such as marine operations and inclusive education.