An opportunity has arisen for a D.Phil. (Ph.D.) place on the BBSRC-funded
Oxford Interdisciplinary Bioscience Doctoral Training Partnership in the
area of Artificial Intelligence, specifically 'Predicting the spread of
antimicrobial resistance from genomics using machine learning'.
If successful in a competitive application process, the candidate will join
a cohort of students enrolled in the DTP’s one-year interdisciplinary
training programme, before commencing the research project and joining
Danny Wilson's research group at the Big Data Institute. See
www.danielwilson.me.uk
This project addresses the BBSRC priority area “Combatting antimicrobial
resistance” by using ML to predict the spread of antimicrobial resistance
in human, animal and environmental bacteria exemplified by Escherichia
coli. Understanding how quickly antimicrobial resistance (AMR) will spread
helps plan effective prevention, improved biosecurity, and strategic
investment into new measures. We will develop ML tools for large genomic
datasets to predict the future spread of AMR in humans, animals and the
environment. The project will create new methods based on award-winning
probabilistic ML tools pioneered in my group (BASTA, SCOTTI) by training
models using genomic and epidemiological data informative about past spread
of AMR. We will apply the tools collaboratively to genomic studies of E.
coli in Kenya, the UK and across Europe from humans, animals and the
environment, Enterobacteriaceae in North-West England, and Campylobacter in
Wales. Genomics has proven effective for asking “what went wrong” in the
context of outbreak investigation and AMR spread; here we will address the
greater challenge of repurposing such information using ML for forward
prediction of future spread of AMR. Scrutiny will be intense because future
predictions can and will be tested, raising the bar for the biological
realism required while producing computationally efficient tools.
Attributes of suitable applicants: Understanding of genomics. Interest in
infectious disease. Some numeracy, e.g. mathematics A-level, desirable.
Experience of coding would help.
Funding notes: BBSRC eligibility criteria for studentship funding applies (
https://www.ukri.org/files/legacy/news/training-grants-january-2018-pdf/).
Successful students will receive a stipend of no less than the standard
RCUK stipend rate, currently set at £14,777 per year.
How to apply: send me a CV and brief covering letter/email (no more than 1
page) explaining why you are interested and suitable by the ***Wednesday 11
July*** initial deadline. (I will invite the best applicant/s to submit
with me a formal application in time for the Friday 13 July second-stage
deadline).
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