Postdoctoral Research Associate in Computational Muscle Biology

About Us

The Centre for Human & Applied Physiological Sciences (CHAPS) is situated within the School of Basic & Medical Biosciences(within the Faculty of Life Sciences and Medicine), which is led by Professor Mathias Gautel and comprises five departments with a wide range of expertise and interests. Using a bench to bedside approach, the School aims to answer fundamental questions about biology in health and disease and apply this knowledge to the development of new and innovative clinical practise, alongside providing a rigorous academic programme for students.

About the role

Dr Seaborne’s group investigates the molecular and cellular mechanisms underpinning muscle biology, in the context of health and disease. His labs interests have begun to explore the analysis of multi-molecule data sets to understand how striated muscle is regulated during health, disease and stress-response contexts.

The successful candidate will lead computational lab analysis work across multiple projects, largely focussing on the perturbation of skeletal muscle cells (myofibres) during periods of environmental and/or genetic influence (ageing, amyotrophic lateral sclerosis, sex differences…) and in the analysis of single myofiber data sets. Importantly, the candidate will be required to use current and bespoke computational and bioinformatic pipelines to analyse these data sets, as well as possibly modify or develop their own approaches to reliably and accurately analyse these data.

This is a 12 month full-time on campus position (Guys Campus, London Bridge) starting no earlier than the 2 February 2026.

About You

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

• PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, or a related quantitative field *
• Demonstrated expertise in programming and scripting (e.g. R, Python, Bash) for data processing, integration and visualisation 
• Proven experience developing and using necessary pipelines for analysis of large-scale bulk and single cell data sets 
• Strong understanding of statistical modelling, data normalisation and machine learning methods applied to biological datasets 
• Experience with data management and version control (Git/GitHub, workflow automation, documentation) 
• Capacity to work independently and as a multi-disciplined team  
• Excellent verbal and written communication skills (e.g. presentations, seminars, lab meetings…) as proven by track-record of scientific publications in leading journals and scientific dissemination 
• Demonstrate excellent organisational skills, record keeping, academic integrity, and rigor

* Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate, and the salary will increase to Grade 6. 

Desirable criteria

• Experience analysing single-myofiber or muscle-based datasets using modern frameworks and pipelines  
• Familiarity with single-cell multi-omic data integration and network or pathway inference tools 
• Experience working in high-performance computing or cloud environments 
• Interest in developing novel computational or statistical methods for muscle biology 
• Enthusiasm for open science — sharing code, data and reproducible research practices 

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