View Vacancy -- MRC Postdoctoral Research Scientist LMS 2731
- 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
- PhD in Data Science, Bioinformatics, or equivalent
- Training in single cell and/or spatial genomics
- Training in machine learning
- 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
- Experience in proteomics and/or metabolomics
- Experience in population genetics
- Experience in developing and applying machine learning tools
- 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
- Familiarity with inflammation, metabolism, senescence and/or ageing biology
- Exposure to human genetics, gene network analyses, and genome biology
- 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
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