Research Associate / Research Fellow in Human Genetics

at  Kings College London

London, England, United Kingdom -

Start DateExpiry DateSalaryPosted OnExperienceSkillsTelecommuteSponsor Visa
Immediate23 Jan, 2025GBP 55694 Annual24 Oct, 2024N/AGood communication skillsNoNo
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Description:

Job id: 097528. Salary: Research Associate: £44,105 - £51,485 per annum, including London Weighting Allowance; Research Fellow: £52,874 - £55,694 per annum, including London Weighting Allowance.
Posted: 22 October 2024. Closing date: 05 November 2024.
Business unit: Faculty of Life Sciences & Medicine. Department: Medical & Molecular Genetics.
Contact details: Professor Michael Simpson. Michael.simpson@kcl.ac.uk
Location: Guy’s Campus. Category: Research.
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About Us
The post will be based in the research group of Professor Michael Simpson a multidisciplinary team with expertise in genetics, genomics, immunology and dermatology. The group is co-located in the Department of Medical and Molecular Genetics and the St John’s Institute of Dermatology at King’s College London at the Guy’s Campus in London Bridge.
About the role
We are seeking a highly motivated postdoctoral researcher with a strong background in statistical genetics and/or computational biology to join our team.
The successful applicant will lead a cutting-edge research programme which aims to understand the genetic and molecular mechanisms of complex inflammatory skin diseases. The applicant will be encouraged to identify and drive a theme within our programme, depending on their expertise, development needs, and interest.
An estimated 20–25% of the population is affected by inflammatory skin diseases, the most common of which include eczema, psoriasis and acne. Easing the burden of these diseases warrants attention owing to their high prevalence, visibility, psychosocial impact, need for long-term treatment and associated costs. Inter-individual variability in response and tolerability of existing treatments further adds to the already substantial impact on quality of life and underlines the need for improved knowledge on pathogenic mechanisms to inform novel drug targets for inflammatory skin diseases.
Over the last 10 years our team has led the identification of the genetic variation that contributes to susceptibility and severity of inflammatory skin diseases and the biological mechanisms through which they operate. We have established collaborations across the globe and built a research programme that leverages high throughput, high-resolution sequencing platforms and novel analytical methods.
This role represents a unique opportunity to generate biological insights from our large-scale research datasets including single-cell multiomic sequencing data from skin and blood to enable the identification of the causal mechanisms of disease, the cell-types in which they occur and the identification of therapeutic targets. There are also substantial opportunities to explore the impact of selection and use genetics as a tool to investigate the potential causal relationship between inflammatory skin disease and mental health traits.
The ideal candidate will have a Ph.D. in computational or statistical genetics and genomics or a related field with a proven track record in the analysis of large biological datasets.
They are expected to be able to work effectively as part of a team but also to direct research independently as required. This post represents an exceptional opportunity for career development within a highly supportive environment for both early- and mid-career researchers.
This is a full time post (35 Hours per week), and you will be offered an fixed term contract for up to 30/09/2026.
About You
To be successful in this role, we are looking for candidates to have the following skills and experience:
Research Associate

Essential criteria

  • PhD awarded in genetic epidemiology, statistical genetics, bioinformatics or related discipline *
  • Strong computational skills, with expertise in scripting in BASH and either R or Python
  • Experience with analysing complex datasets on high-performance compute clusters
  • Demonstrated ability to perform and publish high-impact research
  • Ability to work successfully as part of a team
  • Exceptional presentation skills and data visualisation skills
  • Evidence of self-directed productivity and to meet deadlines
  • 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 of processing and analysis of human genetic and phenotype data, including EHR data
  • Experience in population genetics

Research Fellow

Essential criteria

  • PhD awarded in genetic epidemiology, statistical genetics, bioinformatics or related discipline
  • Postdoctoral research experience in genetic epidemiology, statistical genetics, bioinformatics or related discipline
  • Strong computational skills, with expertise in scripting in BASH and either R or Python
  • Experience with analysing complex datasets on high-performance compute clusters
  • Demonstrated ability to perform and publish high-impact research
  • Ability work successfully as part of a team, to supervise the work of others and to focus team efforts and motivate individuals
  • Exceptional presentation skills and data visualisation skills
  • Evidence of self-directed productivity and to meet deadlines

Desirable criteria

  • Experience of processing and analysis of human genetic and phenotype data, including EHR data
  • Experience in population genetics
  • Knowledge and understanding of competitive research funding and ability to develop applications to funding bodies

Downloading a copy of our Job Description
Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the next page after you click “

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