PhD Studentship: Automated High Throughput Machine Learning-guided Condition Optimisation for Biocatalysis

To achieve net zero in chemicals synthesis and production, low energy approaches are critical. Here, biocatalysis provides not just a low-energy solution, but one which can also be highly selective, important in the production of high purity chiral materials such as pharmaceuticals. Use of biocatalysis at scale has been limited, however, due to the need to optimise low concentrations and slow rates of otherwise promising candidates. Automated high-throughput systems are increasingly important tools for optimising reactions, and application to improving bioprocess through automated optimisation of biocatalyst conditions could pave the way for much more energy and material efficient production of chemicals.

This AstraZeneca-supported project will focus on developing new optimisation approaches that integrate high throughput systems with molecular modelling of biocatalysts and machine-learning approaches. As part of our team, you will help to expand automated and modular monitoring for parameters of interest, automate experimental processes for enzyme mutant production, and combine these with real-time modelling information to produce optimised systems that find both the best enzyme coupled with the best conditions for chemicals production. This will help de-risk and reduce the significant costs currently incurred in bringing a biocatalytic processes to scale. You will also have a range of opportunities to gain valuable transferrable skills (including through our doctoral college), network with international and industry partners, and be well-set up for a role in either industry, data science or academia at the end of the programme.

Funding Details

Additional Funding Information

This AstraZeneca iCASE studentship is for 4 years and provides a tax-free stipend of £19,237 per annum plus tuition fees at the UK rate. Excellent International candidates are eligible for a full international fee waiver however due to UKRI funding rules, no more than 30% of the studentships funded by this grant can be awarded to International candidates.

Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment. Fees are reviewed annually and are likely to increase to take into account inflationary pressures.

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