Research Fellow in the Application of Machine Learning to Optimising Crystallisation Processes

University of Leeds School of Chemical and Process Engineering

Research Fellow in the Application of Machine Learning to Optimising Crystallisation Processes

Are you an ambitious researcher looking for your next challenge? Do you have an established background in machine learning and image analysis (computer vision)? Do you have a passion to apply this knowledge within the process engineering domain? Do you want to further your career in one of the UK’s leading research intensive Universities?

You will be a member of the team of an EPSRC funded multidisciplinary research project: Advanced Crystal Shape Descriptors for Precision Particulate Design, Characterisation and Processing (Shape4PPD). The research work is led by the University of Leeds (Schools of Computing and Chemical & Process Engineering) and involves collaboration with AstraZeneca, Cambridge Crystallographic Data Centre (CCDC), F. Hoffmann-La Roche (International), Infineum UK Ltd, Imperial College London, Keyence (UK) Ltd, Pfizer, Syngenta, Universities of Hertfordshire and Strathclyde.

You will work on the project to apply the latest machine learning and image analysis technologies to process engineering, hence developing a digital design platform for manufacturing processes of crystalline materials and their product performance. You will be highly motivated in exploring the application of the machine-learning-based knowledge and technology in crystallisation process engineering.

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

, Professor of Artificial Intelligence

Tel: +44 (0)113 343 5765 or email:


, Brotherton Professor of Chemical Engineering

Tel: +44 (0)113 343 2408 or email:

Location: Leeds - Main Campus
Faculty/Service: Faculty of Engineering & Physical Sciences
School/Institute: School of Chemical & Process Engineering
Category: Research
Grade: Grade 7
Salary: £34,304 to £40,927 p.a.
Working Time: 37.5 hours per week
Post Type: Full Time
Contract Type: Fixed Term (Up to 2 years (grant funding))
Release Date: Friday 25 February 2022
Closing Date: Sunday 27 March 2022
Interview Date: To be confirmed
Reference: EPSPE1066