Research Associate in Microbial genomics and Bioinformatics – University of Bath

Website University of Bath

Research Associate in Microbial genomics and Bioinformatics – University of Bath

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 attribution
  • Analyse 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 metadata
  • Analysis and interpretation of results
  • Iterative improvement and development of models
  • Contribute to developing new techniques and methods
  • Apply knowledge in a way which develops new intellectual understanding
  • Disseminate research findings for publication, research seminars etc.
  • Supervise students on research related work and provide guidance to PhD students in the group
  • Undertake management/administration arising from research
  • Contribute to Departmental research-related activities and research related administration
  • Develop 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 capability
  • Ability to communicate complex information clearly
  • Fluency in relevant models, techniques or methods and ability to contribute to developing new ones
  • Ability to assess computational resource requirements and use resources affectively
  • Experience of developing bioinformatics software
  • Understanding of basic statistics to be applied in machine learning models
  • Understanding 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.

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Further details:
The University of Bath is an equal opportunities employer and has an excellent international reputation with staff from over 60 different nations. To achieve our global aspirations, we welcome applicants from all backgrounds.

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