About the Project
The supervisory team for this PhD project will be:
Dr Christopher Pudney (Department of Biology & Biochemistry)
Prof Michael Tipping (Bath Institute for Mathematical Innovation)
Dr Daniel Henk (Department of Biology & Biochemistry)
Dr Chris Chuck (Department of Chemical Engineering)
Microbes are used in a vast range of industrial processes to produce commercially valuable products, for example fuels, fine chemicals and in brewing. These living systems can easily become contaminated with microbes that alter or damage the industrial processes, which can be extremely costly. At present there is no routine and quick way to identify these contaminants, with present approaches being unreliable and slow. The project develops a solution based on fluorescence spectroscopy combined with Bayesian machine learning to overcome this commercially expensive bottleneck.
We have developed an approach based on the ultra-violet fluorescence of microbes that provides an accurate chemical ‘fingerprint’ of different microbe species, strains and pathogenic forms. We wish to apply this approach to industrial fermentation processes to continuously monitor the ‘health’ of the fermentation, identifying contamination as it occurs in real-time.
Developing this technology would dramatically alter the control and monitoring of industrial fermentations, making them more efficient and profitable. Implementing the technology would have major impact on specific industrial processes and we will capture the impact thoughout the project. To this end, the project includes two industrial partners that use fermentation to produce commercial products, and we will be studying yeast as the microbial system.
The poject will address two challenges, (i) the integration within an industrial pipeline and (ii) quantitation of our spectral fingerprint.
Integrating the technology into an industrial setting means having a broad library of fingerprints for different microbes that are encountered during industrial fermentation processes and in their different combinations and ratios. We will build the fingerprint library by measure the UV fluorescence fingerprint for different yeast species and strains under different growth conditions.
To leverage the power of these data and to achieve the goal of continuous monitoring, spectral fingerprint variation will be captured by developing a library of generative probabilistic machine learning models. These models may then be deployed as a principled statistical mechanism for discrimination of individual fingerprints. Disambiguating fingerprints derived from varying combinations of microbes will be addressed by combining these generative models using Bayesian inferential techniques.
We will test our technology in a pilot fermentation plant to assess where the technology is best deployed during the fermentation process. We will liaise with our industrial partner at all stages to ensure that industrial process requirements are at the forefront of the development of the technology.
The interdisciplinary project will allow us to develop our technology, which has huge commercial potential, and to cement new collaborative interactions between Biology, Maths and Engineering and our industrial partners. We anticipate high level publishable outcomes relating to the approach as well as industrially relevant data. The project would allow us to develop a platform on which the interdisciplinary team can develop.
The cross-disciplinary training potential for the student is outstanding, with training in contemporary spectroscopy, biophysics, statistics and chemical engineering. The student will have the advantage of being part of the Institue of Mathematical Innovation, which is ideally placed to promote mathematical solutions to industrial problems. We anticipate the student will spend time with the industrial partner to best understand the industrial process requiremnets and their link to the project.
Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree, or the equivalent from an overseas university in mathematics (or any joint mathematics degree), statistics, physics, astrophysics, engineering maths or other science degrees with sufficient baseline maths and programming content. A master’s level qualification would also be advantageous.
Informal enquiries should be directed to Dr Chris Pudney ([Email Address Removed]).
Formal applications should be made via the University of Bath’s online application form for a PhD in Biology:
https://www.bath.ac.uk/samis/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUBB-FP02&code2=0012
More information about applying for a PhD at Bath may be found here:
http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/
Anticipated start date: 1 October 2018
Funding Notes
Some Research Council funding is available on a competition basis to Home and EU students who have been resident in the UK for 3 years prior to the start of the project. For more information on eligibility, see: https://www.epsrc.ac.uk/skills/students/help/eligibility/.
Funding will cover Home/EU tuition fees, a stipend (£14,553 per annum for 2017/18) and a training support fee of £1,000 per annum for 3.5 years. Early application is strongly recommended.
Applicants classed as Overseas for tuition fee purposes are NOT eligible for funding; however, we welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.
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