- Value: This project is open to self-funded students and is eligible for funding in an open competition across the School of Chemistry, see funding schemes for details.
- Number of awards: 1
- Deadline: Applications accepted all year round
It is an unceasing challenge to reduce the time scale for development of new chemical products to the point of reliable manufacture and entrance into the market place. These processes however, are complex with process outcome being affected by a vast number of chemical and physical parameters; e.g. temperature, pressure, reagent stoichiometry, pH, heat and mass transfer affect quality and scalability making the definition of a chemical process at manufacturing scale a very challenging task.
This project aims to develop an Industry 4.0 approach revolutionizing the transfer from laboratory to production using advanced data-rich and cognitive computing technologies. We will develop new algorithms based on Bayesian Optimisation and evolving Kinetic Motifs that merge data analysis and the generation of further experiments. Cloud based machine learning services (hubs) will generate experiment setpoints delivered through the cloud to automated laboratory platforms (LabBots). A key novelty is that the analysis services can receive and analyze results, and post further experiments to the LabBots, thus generating a data generation - data analysis closed-loop. This enables the application of machine learning to chemical development: the system will continuously learn, increasing in confidence and knowledge over time, from previous iterations. Note: Position may be filled before close date and recruitment closed.
Applications are invited from candidates with or expecting a minimum of a UK upper second class honours degree (2:1), and/ or a Master's degree in a relevant science subject such as (but not limited to) chemistry or chemical engineering.
If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements.
How to apply
Formal applications for research degree study should be made online through the university's website. Please state clearly in the research information section that the PhD you wish to be considered for is 'Machine learning for chemical manufacture' as well as Dr Richard Bourne as your proposed supervisor.
We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.
If you require any further information please contact the Graduate School Office e: firstname.lastname@example.org