View Vacancy -- MRC Postdoctoral Research Scientist LMS 2731

Overall purpose:

The MRC Laboratory of Medical Sciences (LMS) is embracing a challenge-led research style, where we structure our projects around big questions and team-powered discovery science. A major LMS initiative is to understand the impact of metabolism and the immune system on age-related health problems, and how this differs in women and men.

The postdoctoral researcher will join a multi-disciplinary team working on a multi-lab team science initiative aimed at understanding and modulating cytosolic nucleic acid sensing pathways involved in chronic inflammation and ageing (“inflammageing”). The successful candidate will work specifically on the analysis of large-scale -omic data from humans and experimental ageing models.

The postholder will employ a range of computational approaches to analyse and integrate single cell and spatial multiomic datasets to unravel the effects of nucleic acid sensing pathways (e.g. cGAS/STING, RIG-I, MDA5 and others) on tissue ageing, inflammation and senescence. Alongside, the postholder will work collaboratively with participating LMS groups to analyse the specific involvement of human genetic variants, gene regulatory networks, and molecular ‘ageing’ clocks. These convergent analyses will reveal potential causal factors, underlying sex differences, and potential intervention points for therapeutics, that will be developed and expanded within a collaborative team science framework.

The postholder will be supervised by Dr. Scott (Genomics of Obesity) with support from Dr. Spivakov (Functional Gene Control) and Prof. O’Regan (Computational Cardiac Imaging). More broadly, this post is part of a Institute wide collaboration and the postholder will be tightly coordinated and involved in meetings with other PIs and researchers working in the project.

Main duties / key responsibilities:

• Analyse and interpret single-cell and spatial-omics data (e.g., single cell multi-omics, CITE-seq, spatial transcriptomics) from aged human and mouse tissues, and multicellular experimental models
• Develop and apply bioinformatics pipelines to integrate multi-omic datasets and identify conserved ageing signatures across experimental models
• Build and analyse gene regulatory networks from tissue ageing and reference datasets
• Intersect findings with Cell Census Atlases to delineate cross-tissue ageing and inflammation signatures
• Collaborate with groups investigating the triggers of inflammation and damage in ageing tissues (e.g. transposable elements and structural variants)
• Work with groups doing genetic association analyses in UK Biobank to identify genetic variants that cause human ageing phenotypes.
• Contribute to the development of "ageing clocks" from molecular and imaging data
• Collaborate with other scientists working on molecular and cellular models of human ageing, inflammation and metabolism
• Provide training and support to junior lab members, including PhD students
• Participate in regular lab meetings, journal clubs, and institute seminars
• Write scientific papers and present findings at national and international conferences
• Take an active part in the academic activities of the LMS
• Form good working relationships with other members of LMS groups as well as internal and external collaborators

Education / Qualifications / Training required (will be assessed from the application):

Essential:

• PhD in Data Science, Bioinformatics, or equivalent
• Training in single cell and/or spatial genomics

Desirable:

• Training in machine learning

Previous work experience required (will be assessed from the application and at the interview):

 Essential:

• Expertise in genomic data science
• Experience in single cell and/or spatial genomic data analysis
• Exposure to machine learning methods
• Expertise in data management and curation
• Experience of building and optimising data analysis tools and pipelines
• Coding expertise in several programming languages (particularly Python and R).
• Track record of publishing scientific results in peer-reviewed journals

Desirable:

• Experience in proteomics and/or metabolomics
• Experience in population genetics
• Experience in developing and applying machine learning tools

Knowledge and experience (will be assessed from the application and at the interview):

Essential:

• Understanding of single cell and spatial biology, ideally in the contexts of inflammation, metabolism or ageing
• Knowledge of biostatistics
• Skills in advanced computation and machine learning
• Knowledge of, and interest in, human ageing and age-related diseases

Desirable:

• Familiarity with inflammation, metabolism, senescence and/or ageing biology
• Exposure to human genetics, gene network analyses, and genome biology

Personal skills/behaviours/qualities (will be assessed at the interview):

Essential:

• Capability and willingness’s to work in a team science environment
• Excellent computational skills and record keeping
• Excellent organisational skills
• Excellent attention to details
• Excellent creativity
• High-Motivation and perseverance
• The post holder should be prepared to work unsociable hours as work demands from time to time
• Ability and willingness to adapt to changing timetables or work patterns as dictated by changing circumstances
• Proven ability to prioritise, plan ahead and manage a high volume of work to meet deadlines, both prescribed and self-imposed
• Ability to use own initiative and work independently and as part of a team, as required
• Problem solving abilities
• Proven multitasking abilities
• Ability to work in a collaborative research environment
• Willingness to give and receive help with challenging problems
• Good verbal communication skills in English
• Ability to communicate its science in a clear way
• Good interpersonal skills
• Willingness to share knowledge and transfer skills to other members of the group on a daily basis

Please upload CV, names and contacts of two scientific references along with a cover letter stating why you are applying for this role (providing evidence against the requirements of the job as per the job description and person specification). Please quote reference number LMS 2731.

Please note that applications may be reviewed by both LMS and Imperial staff