|Salary:||£40,011 to £45,437 per annum, inclusive|
|Placed On:||21st August 2019|
|Closes:||22nd September 2019|
We are seeking to appoint a Research Fellow to apply machine learning and artificial intelligence techniques in order to make informed decisions in early stage drug discovery of the effects of mutations that might lead to new infectious disease drugs becoming less effective and thereby improving the longevity of these drugs.
This exciting post is funded by the Medical Research Council (MRC) and is directed by Dr. Nicholas Furnham in collaboration with Professor Taane Clark. The successful applicant will exploit existing as well as develop new computational tools for automatically analysing the molecular consequences of single nucleotide polymorphisms (SNPs) linked with therapeutic resistance from genome wide association studies (GWAS). They will use this wide-ranging iterative analysis to build a predictive model to identify future SNPs that could lead to therapeutic failure.
The project leverages the wealth of in-house and publicly GWAS (viral, bacterial, parasitic) linking SNPs to drug resistance. By exploiting the latest state-of-the art tools for predicting various measures of the effect of a mutation the applicant will use machine leaning to anticipate mutations leading to resistance before they become fixed in a given pathogen population. The tools developed will be used to make an informed decision in early stage drug discovery as to the effects of mutations on a potential drug binding region that might lead to a new drug becoming less effective. The new tools will be applied by us and collaborators at the Dundee Drug Discovery Unit within our existing drug discovery programs against a range of infectious disease agents.
The successful applicant will have a postgraduate degree, ideally a doctoral degree, in a relevant field, relevant experience in bioinformatics or computational biology, contributions to written output, preferably peer-reviewed, as expected by the subject area/discipline in terms of types and volume of outputs, proven experience in programming in Python or R as well as working in a Unix environment. Further particulars are included in the job description.
This full-time post, based in London, is funded by the MRC until 31st October 2022. Salary is on the Academic Pathway Grade 6 scale in the range £40,011 - £45,437 per annum (inclusive of London Weighting). The post will be subject to the LSHTM terms and conditions of service. Annual leave entitlement is 30 working days per year, pro rata for part time staff. In addition to this there are discretionary “Director’s Days”. Membership of the Pension Scheme is available.
Applications should be made online via our website at jobs.lshtm.ac.uk. Online applications will be accepted by the automated system until 10pm on the closing date. Any queries regarding the application process may be addressed to email@example.com.
The supporting statement section should set out how your qualifications, experience and training meet each of the selection criteria. Please provide one or more paragraphs addressing each criterion. The supporting statement is an essential part of the selection process and thus a failure to provide this information will mean that the application will not be considered. An answer to any of the criteria such as “Please see attached CV” will not be considered acceptable.
Please note that if you are shortlisted and are unable to attend on the interview date it may not be possible to offer you an alternative date.
The London School of Hygiene & Tropical Medicine is committed to being an equal opportunities employer.
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