Postgraduate research opportunities

Data-driven approaches for in-line monitoring of particle attributes in chemical and pharmaceutical manufacturing processes

The project will investigate Machine Learning and Deep Learning approaches to extract and fuse multiple data streams from multi-sensor setups in chemical and pharmaceutical manufacturing through a combination of experimental work, data analytics and process simulation.

Number of places



3 March 2021


3 years


Students applying should have (or expect to achieve) a high 2.1 undergraduate degree in a relevant Engineering/science discipline, and must be highly motivated to undertake multidisciplinary research. 

Knowledge and/or experience in particle technology, material characterisation, data analytics, machine learning and programming skills (e.g. Python, Matlab, PyTorch/TensorFlow) are desirable.

Project Details

Particle processing is widely used in chemical and pharmaceutical manufacturing industries. In this context, particle attributes influence processability and are key to the optimisation of product quality. However, despite the high material costs involved, significant process inefficiencies are still common in these sectors. New technologies that improve the monitoring of particle attributes are essential to transform the ability to understand and control pharmaceutical processes and to achieve the reliability and stable operation of other sectors such as aerospace and automotive.

Currently, particle attributes are mainly characterised using off-line techniques that are prone to particle alteration during sampling, transport and analysis. In-line measurements are quickly developing as a fast alternative to overcome these limitations and have the potential to provide a more representative view of the particle population in-situ. However, unsolved challenges still remain in the extraction of quantitative particle attributes due to the complex in-line measurement environment.

The project will use a combination of experiments, data analytics and simulation to provide more accurate representation of particle attributes from in-line measurements. Data will be captured using state-of-the-art Process Analytical Technologies (PAT) available at the Centre for Continuous Manufacturing and Crystallisation (CMAC), including Particle View Microscopy (PVM), Focused-Beam Reflectance Measurement (FBRM) and Raman spectroscopy. These data streams will inform the development of Machine Learning and Deep Learning models to extract more representative particle size, shape and morphology distributions, as well as solution solid loading. Simulations of the measurement environment will contribute to identifying deviations from ideal scenarios and to providing physical meaning to these anomalies. While extracting information from individual sensors is a challenge in itself, the project will aim to implement data fusion approaches to further enhance in-line quantification of particle attributes and inform more advanced process control strategies.

In addition to undertaking cutting edge research, students are also registered for the Postgraduate Certificate in Researcher Development (PGCert), which is a supplementary qualification that develops a student’s skills, networks and career prospects.

Funding Details

This PhD project is initially offered on a self-funding basis. However, excellent candidates will be considered for a University scholarship.


Contact us

James Weir Building, 75 Montrose Street, Glasgow, G1 1XJ

How to apply

Apply for this project here – please quote the project title in your application.

During the application you'll be asked for the following information and evidence uploaded to the application:

  • your full contact details
  • transcripts and certificates of all degrees
  • proof of English language proficiency if you are not from a majority English-speaking country as recognised by UKVI
  • two references, one of which must be academic. Please see our guidance on referees
  • funding or scholarship information
  • international students must declare any previous UK study

By filling these details out as fully as possible, you'll avoid any delay to your application being processed by the University.

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