Bioinformatician

About us:

The Department of Medical and Molecular Genetics at King’s College London is at the forefront of human genetics research. We are part of the School of Basic and Medical Biosciences within the Faculty of Life Sciences and Medicine at King’s College London. This post is based in the Department of Medical and Molecular Genetics and supervised by Professor Matthew Brown, working in close collaboration with Professor Emma Duncan of the Department of Twin Research & Genetic Epidemiology. Their collaborative research group focuses on unlocking the genetic basis of common and rare bone and joint diseases, including ankylosing spondylitis and related conditions. We have access to state-of-the-art facilities and are embedded within a global network of collaborators.

About the role:

We are seeking a talented and motivated Bioinformatician to join our research group in the Department of Medical and Molecular Genetics at King’s College London. You will play a pivotal role in advancing our understanding of common and rare bone and joint diseases, including the common arthopathy ankylosing spondylitis and related diseases. Supervised by Professor Matthew Brown (Medical and Molecular Genetics) and working closely with Professor Emma Duncan (Twin Research & Genetic Epidemiology), you will harness advanced bioinformatics approaches to decode the genetic underpinnings of these conditions. This will include identification of new disease-associated loci, investigation of functional mechanisms of disease, research into polygenic risk scores (including clinical implementation), & identification of novel therapeutic interventions & personalised treatment strategies. You will collaborate with our global network of researchers and contribute to high-impact publications and grant applications.

Hybrid working is possible, with the postholder expected to work on-site for approximately 2 days per week.

This is a full-time post  and you will be offered a fixed-term contract for 1 year.

Research staff at King’s are entitled to at least 10 days per year (pro-rata) for professional development. This entitlement, from the Concordat to Support the Career Development of Researchers, applies to Postdocs, Research Assistants, Research and Teaching Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information.

About you:

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

Essential criteria

1. PhD qualified in bioinformatics with a focus on genetic studies of common and/or rare human diseases.
2. Proficiency in programming languages used in bioinformatics, such as Python and/or R, with the ability to develop & implement computational solutions for genetic data analysis
3. In-depth knowledge and hands-on experience with bioinformatics tools & databases relevant to genetic research, including tools for gene mapping, functional genomics analysis, & polygenic risk score studies
4. Excellent written and verbal communication skills, with the ability to convey complex genetic concepts to both technical and non-technical audiences, and a proven ability to contribute to scientific publications
5. Ability to adapt to evolving research priorities and methodologies, and demonstrated openness to incorporating new technologies & approaches
6. Strong organisational & project management skills, with the ability to plan, execute, and deliver research objectives effectively within specified timelines

Desirable criteria

1. Track record of successful collaboration in research projects, ideally in interdisciplinary teams working with researchers, clinicians, & global collaborators
2. Experience translating genetic data into actionable insights with potential clinical applications, including understanding the implications of genetic findings for therapeutic development
3. Previous experience with laboratory-based functional genomics methodologies, demonstrating practical understanding of integrating wet-lab techniques with computational analyses

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