View Vacancy -- MRC Postdoctoral Research Scientist LMS 2733

Team Science:

The MRC Laboratory of Medical Sciences (LMS) is proud to launch its new Team Science initiative, marking a bold step toward a more collaborative, challenge-driven model of research.  

Scientific breakthroughs are often portrayed as the work of lone geniuses, but in reality, they are usually the result of collective effort. A Team Science project is not just bigger or multi-part, it’s fundamentally different in its ethos and operational model. It’s designed to foster true collaboration across disciplines, academic institutions, industry and the public, creating something that could not be achieved alone. It’s about collective ambition, shared infrastructure and interdisciplinary innovation. 

This new approach places collaboration and real-world impact at its core, integrating insights from across the LMS’ existing expertise and diverse perspectives. 

Overall purpose:

A new exciting opportunity has arisen for a 3 Year Postdoctoral Research Scientist to join the Reproductive Health and Fertility: Ageing, Sex and the Environment Team science project led by J Vaquerizas, E Martinez-Perez, and P Hajkova. Reproductive health is an important aspect of human wellbeing, yet infertility affects approximately one in six people globally. Despite its prevalence and significant emotional, societal and economic impact, the biological mechanisms underlying infertility and reproductive ageing remain poorly understood. We have put together a team of internationally-leading experts in germline biology and genome function that will use transdisciplinary experimental approaches to understand how intrinsic and extrinsic factors influence female and male fertility and reproductive ageing.

We are looking for a talented and self-motivated computational biologist with an interest in reproductive biology and genome function to join the Reproductive Health and Fertility: Ageing, Sex and the Environment Team science initiative as a postdoctoral researcher. The main goal of the project will be to analyse and integrate multimodal genome-wide datasets to build predictive models of healthy reproductive processes and investigate how these are perturbed in infertility and ageing. The candidate will work closely with other postdocs and students in the Team who will use omics and imaging approaches to elucidate the genetic and molecular causes of infertility, the impact of aging in male and female fertility, and the effect of metabolism and germline-soma crosstalk on fertility.  

Main duties / key responsibilities:

• Design, plan, and conduct high-quality, independent research aligned with the project aims
• Critically analyse, interpret, and present experimental data with scientific rigor and reproducibility
• Stay current with relevant scientific literature and integrate new findings and methodologies into ongoing research
• Collaborate effectively with internal lab and Team members and external partners, including participation in interdisciplinary projects
• Provide training and advice for other group members including PhD students
• Write-up and publish results in high-quality, peer-reviewed scientific journals
• Take an active part in the academic activities of the LMS
• Attend and contribute to lab meetings, journal clubs and seminar presentations
• Present data at national and international meetings (where and when appropriate)
• Form good working relationships with other members of the group as well as internal and external collaborators

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

Essential:

• PhD (or equivalent experience) in computational biology or in a relevant field (e.g., bioinformatics, molecular biology, cell biology, biochemistry, developmental biology, genetics)
• A first-author manuscript published or submitted from PhD work

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

Essential:

• Proven experience in single-cell omics data analysis and visualisation
• Proficiency in Python and/or R
• Experience with statistical analysis, machine learning methods, and high-performance computing

Desirable:

• Experience with spatial transcriptomics, 3D genome analysis, or epitranscriptomics
• Experience with multiomic data integration techniques
• Integration of transcriptomic, epigenomic, and/or proteomic data
• Network analysis and trajectory modelling
• AI-assisted mutation prioritisation

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

Essential:

• Strong conceptual understanding of genomics, gene regulation, developmental biology, computational biology approaches
• Experience in generating high-quality figures for publication
• Excellent written communication skills in English
• Evidence that the candidate can plan and carry on a research project

Desirable:

• Strong publication record and exposure to scientific meetings
• Experience with GWAS and regulatory variant interpretation
• Strong programming and statistical modelling skills
• Experience in assembling manuscripts for publication
• Experience presenting scientific data at national or international conferences

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

Essential:

• Enthusiastic and motivated, with a genuine interest in scientific discovery and learning
• Independent research capability, including the ability to manage and drive projects autonomously
• Collaborative mindset, with the ability to work effectively in a team-oriented research environment
• Eagerness to learn, particularly in computational and analytical approaches
• Creativity and problem-solving skills, especially when troubleshooting complex challenges
• Excellent verbal communication skills in English, with the ability to present ideas clearly and confidently
• Good interpersonal skills, including the willingness to give and receive help and feedback
• Commitment to knowledge sharing and mentoring within the group
• Highly organised and diligent, with strong attention to detail
• Adaptability and openness to integrating new techniques and collaborating across disciplines
• Excellent laboratory skills
• Excellent organisational skills
• Excellent attention to details
• Excellent creativity
• High-Motivation and perseverance
• Ability and willingness to adapt to changing timetables or work patterns as dictated by changing circumstances
• Ability to work under pressure while maintaining a high degree of accuracy
• 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 your 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 2733.

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