Job Description

An exciting opportunity has arisen for a postdoctoral scientist to develop bioinformatic and machine learning approaches to enrich and explore metabolomic data in global online repositories.
Metabolomics is the study of the small molecules \xe2\x80\x93 metabolites - which provide the building blocks and energy to support life. Levels of metabolites can reveal a great deal about the health of the individual, and thus metabolomics underpins many approaches to personalised and stratified medicine. Metabolomic assays yield 100-1000s of measurements but it is hard to annotate these as known metabolites. This problem is acknowledged as one of the grand challenges of metabolomics. A vast amount of metabolomic data is stored on public repositories, but because of the annotation problem, much of this data cannot be used for biological investigations. In the BBSRC/NSF-funded MARIANA2 project we are connecting two of the largest online repositories: Metabolights (UK) and GNPS (USA) to increase levels of metabolite annotations in repository data sets. We are using cutting-edge computational algorithms to reanalyse existing data and demonstrate how increased annotation coverage improves integration and interpretation of these data.
This post will demonstrate the power of enriched annotation by developing methods of integrating datasets which could not be combined before. The postholder will explore novel methods for data integration, such as disease-metabolite networks, multiview embeddings, and pathway driven models. The post holder will work closely with various teams across the Division, testing, adapting and developing new algorithmic approaches to accomplishing these goals. With input from other members of the project at the University of Birmingham, the European Bioinformatics Institute, and the University of California San Diego, they will apply these tools to the data from the Metabolights and GNPS databases, to provide both integrated data and a suite of software tools which will be of benefit to the wider scientific community.
The postholder will be embedded within the Division of Systems Medicine, home to one of the largest centres for metabolomic science in the world. The Division hosts the MRC-NIHR National Phenome Centre and the NIHR BRC Genomics Facility, and a staff of over 100 researchers actively working on metabolomics, using both experimental and computational approaches.

Duties and responsibilities

The postholder will develop and adapt computational pipelines for integration and annotation of data residing in international repositories, such as EMBL-EBI Metabolights and UCSD GNPS. They will be responsible for development, test, performance evaluation and deployment of the software. They will work with the EBI and UCSD teams to deploy the pipeline to the Metabolights / GNPS sites. The work will involve development of novel methods and the postholder will be responsible for writing this up for publication. Other duties include software documentation, code maintenance, training users and presentation of the research at local and international scientific meetings

Essential requirements

• Have a PhD in bioinformatics or related field with experience in processing, modelling and interpreting LCMS based metabolomic data.
• Familiar with LCMS in metabolomics, including cheminformatic approaches to annotation, machine learning methods for data integration and have good coding skills (preferably Python).
• Have a track record of working successfully in multi-disciplinary teams and publishing your work in peer-reviewed journals.

Further information

This is a full-time post is available until 31/12/24. Informal inquiries can be directed to Prof. Tim Ebbels t.ebbels@imperial.ac.uk. *Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range \xc2\xa340,694 - \xc2\xa343,888 per annum. For technical issues when applying online please email sjobs@ic.ac.uk

Documents

• JD ML.pdf