Imperial College London


Faculty of EngineeringDyson School of Design Engineering

Senior Lecturer



+44 (0)20 7594 8853n.li09 Website




1M03Royal College of ScienceSouth Kensington Campus





Dr Nan Li’s research addresses one of the major challenges facing the transport industry world-wide: developing technological breakthroughs in the manufacturing and design of high-performance lightweight vehicles for a more environmentally-friendly footprint. Her current main research focuses are:

  • Developing innovative manufacturing processes for lightweight structures. She has conducted extensive research work on developing novel low-cost hot forming processes for high-strength, lightweight and complex-shaped panel components for a family of advanced lightweight materials, e.g. ultra-high strength steels, Al alloys, Ti alloys, and Polymer Matrix Composites.
  • Lightweight structural design and optimisation. Key enablers proposed by Dr Nan Li: New design methods relating to hierarchical optimisation and manufacturing constraints; advanced Design-Manufacturing-Integrated modelling; and Machine-Learning empowered Design for Manufacturing.

All of Dr Nan Li’s research projects address real industrial needs, and tackle scientific and technological challenges in materials processing, manufacturing processes, and structural designs, through fundamental studies based on experimental, analytical, and numerical methodologies.

The applications of Dr Nan Li’s research are primarily in automotive and aerospace industries.

Dr Nan Li is highly motivated to explore technological innovations, particularly in relation to the design of novel manufacturing processes, mechanical testing methods and apparatus, and lightweight vehicle structures. She has authored 60 publications and contributed 10 patents. Her proposed research on producing vehicle lightweight structures is favoured by a wide range of industrial collaborators, including SAIC MOTOR (UK and China), Aisin Takaoka (Japan), TATA Steel (Europe), AP&T (Sweden), Lotus (UK), PAB Coventry (UK), Impression Technologies (UK), ESI (France), AVIC (China), Shougang (China), Doncasters (UK), Monolith AI (UK), etc. Nan was awarded the ‘Rowbotham Medal’ 2017 by the Institute of Materials, Minerals and Mining (IOM3) in recognition of her outstanding contribution to the development of the innovative use of materials for automotive applications. She was also recognized as one of the ‘Top 50 Women in Engineering under 35’ in 2017.  


Interested PhD applicants should send an up-to-date CV to Dr Nan Li, with GPA included. Suitable candidates will be required to complete an electronic application form at Imperial College London in order for their qualifications to be addressed by College Registry. Qualified candidates will be interviewed in due course.

2024 Opportunities – please click the links below to find more details

PhD Scholarships/Studentships:

PhD Studentship in AI Driven Design for Forming

PhD Studentship in Lightweight Structural Design and Manufacturing (topic TBD)

Development of AI-driven Tools for Vehicle Structural Design accounting for Manufacturability

Development of a Smart Production System for Forming High-Performance Vehicle Components from Recycled Materials

Design for Sustainable Forming utilising Recycled Fibre Reinforced Polymer (FRP) Composites for Lightweight Structural Components

I am also happy to support your application for other PhD scholarships from Imperial College London

Research Associate/Assistant Post:

Developing AI-Driven Models for Automotive Material Selection and Manufacturability Assessment Using Deep Learning and Physics-Based Simulation Databases




Wang H, Ding Z, Chen X, et al., 2024, Experimental characterisation and constitutive modelling of the intra-ply tensile and shear properties of unidirectional fibre reinforced thermoplastics (UD FRTPs) under solid-state stamp forming conditions, Composites Part A: Applied Science and Manufacturing, Vol:179, ISSN:1359-835X

Attar HR, Foster A, Li N, 2023, Development of a deep learning platform for sheet stamping geometry optimisation under manufacturing constraints, Engineering Applications of Artificial Intelligence, Vol:123, ISSN:0952-1976, Pages:1-23

Attar HR, Foster A, Li N, 2023, Implicit neural representations of sheet stamping geometries with small-scale features, Engineering Applications of Artificial Intelligence, Vol:123, ISSN:0952-1976, Pages:1-21


Attar HR, Lei Z, Li N, 2023, Deep learning enabled tool compensation for addressing shape distortion in sheet metal stamping, 14th International Conference on the Technology of Plasticity, Springer, Cham

Zhu L, Li N, 2023, Springback prediction for sheet metal cold stamping using convolutional neural networks, 2022 Workshop on Electronics Communication Engineering (WECE 2022), SPIE, Pages:1-6

More Publications