Dr Dongda ZhangCantab, DIC, AMIChemE, AMRSC

Lecturer in Bioprocess Systems Engineering and Machine Learning

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Dr. Dongda Zhang is a University Lecturer at the School of Chemical Engineering and Analytical Science at the University of Manchester and an Honorary Research Fellow at the Centre for Process Systems Engineering at Imperial College London. He currently leads research activities in the field of Bioprocess Systems Engineering and Machine Learning at the Centre for Process Integration, and is the University's IChemE Representative.

He holds BSc degree (2011) from Tianjin University and MSc (Distinction) degree (2013) from Imperial College London. He started his PhD research at the University of Cambridge in 2013, completed his research within 2 years alongside over 10 original publications in leading chemical engineering and biotechnology journals, and graduated at the beginning of the third year after the university special approval for Thesis Early Submission. Upon the completion of his PhD in 2016, he was invited by the Chinese Academy of Sciences and several universities for short research visits, and then moved to the Centre for Process Systems Engineering at Imperial College London as a postdoctoral research associate. In 2017, he was awarded the prestigious Leverhulme Early Career Fellowship at the University of Cambridge, followed by his appointment at the University of Manchester as a University Lecturer in the same year.


  • PhD in Chemical Engineering, University of Cambridge, 2013-2016
  • MSc in Advanced Chemical Engineering, Imperial College London, 2012-2013
  • BSc in Chemical Engineering and Technology, Tianjin University, 2007-2011

External positions

Honorary Research Fellow, Imperial College London (University of London)

1 Feb 201831 Jan 2021

Areas of expertise

  • TP Chemical technology - Process Systems Engineering, Biochemical Engineering, Machine Learning, Dynamic Modelling and Optimisation, Renewable Energy, Complex Biosystems, Synthetic Biology, Fermentation Technology, Digital Economy, Data-driven Modelling, Computational Fluid Dynamics, Life Cycle Assessment

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