Research Associate in Microbial genomics and Bioinformatics (fixed-term post) (CC7640)

We seek to recruit a full-time postdoctoral Research Associate in Microbial genomics and Bioinformatics to work in the laboratory of Dr. Lauren Cowley on an Academy of Medical Sciences springboard scheme funded grant in collaboration with the Gastrointestinal Bacterial reference services at Public Health England (PHE).

You will be working on novel machine learning models to predict geographical source attribution from sequencing data of Shiga-toxigenic Escherichia coli and Salmonella. You will be responsible for training, testing and development of prediction models on PHE provided sequencing data to help research the possibilities of using sequencing data to provide automatic prediction of where foodborne disease has originated from; either as a returning traveller, imported food or domestic case.

The position is funded at £39,152 and we expect to appoint at this starting salary for a fixed-term period of 15 months.

Summary of Role

Work within specified research grant to develop novel models for source attributionAnalyse and interpret research findings and results ready for publication

 Main Duties/Responsibilities

Development of source attribution prediction models using machine learning algorithms applied to PHE provided sequencing and metadataAnalysis and interpretation of resultsIterative improvement and development of modelsContribute to developing new techniques and methodsApply knowledge in a way which develops new intellectual understandingDisseminate research findings for publication, research seminars etc.Supervise students on research related work and provide guidance to PhD students in the groupUndertake management/administration arising from researchContribute to Departmental research-related activities and research related administrationDevelop research objectives and proposals for own or joint research, with assistance of a mentor if required

You should hold or be close to completing a PhD in microbiology, genomics, bioinformatics, computer science, applied mathematics or computational biology, with some experience in the development of machine learning prediction models and processing of large microbial sequencing datasets.

High level analytical capabilityAbility to communicate complex information clearlyFluency in relevant models, techniques or methods and ability to contribute to developing new onesAbility to assess computational resource requirements and use resources affectivelyExperience of developing bioinformatics softwareUnderstanding of basic statistics to be applied in machine learning modelsUnderstanding of and ability to contribute to broader management/administration processes

We value, promote and celebrate inclusion, challenging discrimination and putting equality, diversity and belonging at the heart of everything we do. We aim to be an inclusive university, where difference is celebrated, respected and encouraged. We truly believe that diversity of experience, perspectives, and backgrounds will lead to a better environment for our employees and students, so we encourage applications from all genders, backgrounds, and communities, particularly from under-represented groups such as Black and Minority Ethnic (BAME) and disabled people, and value the positive impact that will have on our teams.

We have made a positive commitment towards gender equality and intersectionality receiving a Bronze Athena SWAN award, and we are actively working towards a Silver award. We are a family-friendly University, with an increasingly agile workforce, we are open to flexible working arrangements. We’re also proud to be a disability confident employer and are happy to discuss any reasonable adjustments you may require.

Closing Date: 12 Jul 2020
Type: Education & Research