This project based in Dr Quince’s group at the Earlham Institute (https://www.earlham.ac.uk/quince-group) will use machine learning to develop new computational methods for the integrated analysis of metagenomics and metabolomics data from the human microbiome. These will be used to determine the interaction between nutrition and the microbiome within the gut using the specific example of dietary treatment of Crohn’s disease.
The human gut microbiome is a diverse community of micro-organisms that plays a key role in health and disease. Metagenomics can reveal the functional capability of the community and metabolomics the chemical compounds present but we lack methods for their integrated analysis. We will exploit large-scale paired fecal metagenome and metabolome data from clinical trials of diets for Crohn’s disease to devise methods incorporating prior information on metabolic networks through Bayesian probabilistic models. We will augment this data with bench-top experiments using artificial colon systems.
This studentship would suit an individual with a good degree (minimum 2.1 in or equivalent to a UK Bachelors Honours degree) in a quantitative subject (e.g. Computer Science, Maths/Stats or Physics) wishing to develop expertise in machine learning with a clinically relevant application, or a biologist with strong computational skills. The student will be based at the Earlham Institute (Norwich) but will also have a placement at IBM Research (Daresbury). This represents a unique opportunity to develop academically and industrially relevant computational skills in a growth scientific area. Interested applicants should contact Dr Christopher Quince: [Email Address Removed]. Available start date: 30th September 2022.
For further information and to apply, please visit our website: http://www.earlham.ac.uk/application-guidance
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