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Using machine learning to tell the time (HALLA_E22DTP)


   Graduate Programme

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  Prof A Hall  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

This PhD project aims to take a machine learning approach to accurately predict complex traits in plants. Many of the phenotypes and traits that biologists measure are complex, time consuming and require a specific skill set. One powerful approach to getting around this is to develop robust proxy measurements.

The project will initially develop a model to predict internal biological time using transcriptomic data, to identify a proxy gene set for which the expression can accurately predict the biological time the sample was taken. Over the last 10 years transcriptomic analysis has become a simple, robust, and relatively cheap assay. The proxy gene set will be used across public transcriptomic data sets to investigate how genotype and environment affect biological time.

The work will build on a paper published this year (Gardiner et al. 2021), which applied machine learning to predict complex temporal circadian gene expression patterns in the model plant Arabidopsis thaliana.

The project will go on to develop proxy gene sets for other important traits, such as crop yield, resilience, disease resistance and nitrogen use efficiency. The PhD will provide training in machine learning and bioinformatics.

The student will be based at the Earlham Institute on Norwich Research Park, a centre of excellence for genomics and data-driven research with cutting edge sequencing and computing facilities and excellent scientific training. 

The Norwich Research Park Biosciences Doctoral Training Partnership (NRPDTP) is open to UK and international candidates for entry October 2021 and offers postgraduates the opportunity to undertake a 4-year PhD research project whilst enhancing professional development and research skills through a comprehensive training programme. You will join a vibrant community of world-leading researchers. All NRPDTP students undertake a three-month professional internship placement (PIPS) during their study. The placement offers exciting and invaluable work experience designed to enhance professional development. Full support and advice will be provided by our Professional Internship team. Students with, or expecting to attain, at least an upper second class honours degree, or equivalent, are invited to apply.

This project has been shortlisted for funding by the NRPDTP programme. Shortlisted applicants will be interviewed on Tuesday 25th January, Wednesday 26th January and Thursday 27th January 2022.

Visit our website for further information on eligibility and how to apply: https://biodtp.norwichresearchpark.ac.uk/

Our partners value diverse and inclusive work environments that are positive and supportive. Students are selected for admission without regard to gender, marital or civil partnership status, disability, race, nationality, ethnic origin, religion or belief, sexual orientation, age or social background.


Funding Notes

This project is awarded with a 4-year Norwich Research Park Biosciences Doctoral Training Partnership (NRPDTP) PhD studentship. The studentship includes payment of tuition fees (directly to the University), a stipend for each year of the studentship (2021/2 stipend rate: £15,609), and a Research Training Support Grant for each year of the studentship of £5,000 p.a.

References

Gardiner L-J, Rusholme-Pilcher R, Colmer J, Rees H, Crescente JM, Carrieri AP, Duncan S, Pyzer-Knapp EO, Krishna R & Hall A (2021) Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function. Proc Natl Acad Sci USA 118:
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