Job description

A Postdoctoral Research Associate (PDRA) – Computational biology position is available in the Centre for Gene Therapy and Regenerative Medicine, King’s College London. This is an exciting opportunity to join an interdisciplinary team of scientists working on a Wellcome Trust funded research programme aimed at studying the mammalian germ cell niche in its complexity using a combination of cutting-edge cellular / molecular models and computational approaches.  

The postdoc will work under the supervision of Dr. Mahesh Sangrithi and play a key role in setting up an innovative inter-disciplinary strategy able to analyse and integrate different omics data sets, and further amalgamating these with phenotypic data. The project will employ state of the art genomics, statistical and bioinformatics approaches to discover factors and pathways that regulate germline development.  

The post holder will be involved with the analysis and integration of high throughput genomics data, such scRNA-seq, scATAC-seq, ChIP-seq as well as spatial data. They will also contribute to the overall regenerative medicine theme via participation in projects with scientists and other bioinformaticians in the host lab and the department.  

We are looking for an ambitious candidate with established expertise in bioinformatics, specifically dealing with large omics data sets and data integration. Their will have solid programming skills, expert knowledge of scripting languages such as R and / or Python and proficiency in working on high-performance Linux clusters.  

Applicants should have a recent Ph.D. degree or should have submitted their thesis. 

For informal enquiries regarding suitability for the position, please contact the group leader Dr. Mahesh Sangrithi (Mahesh.sangrithi@kcl.ac.uk ). 

  https://www.kcl.ac.uk/research/reproductive-biology-and-stem-cells-lab 

About the Faculty:

 King’s College London Faculty of Life Sciences and Medicine is one of the largest and most successful centres for research and education in the UK. It consists of over 2000 staff and 5000 students within seven Schools and two Schools of Education. 

Centre for Gene Therapy and Regenerative Medicine (CGTRM) Department: (  https://www.kcl.ac.uk/bmb/our-departments/centre-for-gene-therapy-and-regenerative-medicine) 

CGTRM is located on the Guy’s Hospital Campus in central London. It acts as a nucleus for vibrant research community spanning the entire University and the NHS Foundation Trusts of King’s Health Partners Academic Health Sciences Centre. The goal of CGTRM is to bring together researchers with diverse skill sets and a common interest in elucidating the extrinsic and intrinsic signals that regulate stem cell behaviour. 

Research Computing Infrastructure at KCL:

  https://rosalind.kcl.ac.uk/ 

  https://www.nvidia.com/en-gb/industries/healthcare-life-sciences/cambridge-1/ 

ABOUT THE KCL / NIHR Biomedical Research Centre:

Within the Biomedical Research Centre (BRC) at Guy's  and St Thomas' NHS Foundation Trust the Population Science Cluster, led by Professor Robin Ali, provides interdisciplinary skills in public health, primary care, epidemiology, statistics, health economics and social sciences for research and training. 

  https://www.kcl.ac.uk/lsm/research/divisions/hscr/research/biomedical-research-centre-(brc) 

This post will be offered on a fixed term for 1 year, and potentially be extended for up to 3 yrs.  

Excellent candidates from the EU and internationally are welcome to apply, and may be considered via the Global Talent visa scheme. 

From: 1 May 2022 

This is expected to be a full-time post (100% full time equivalent).

Key responsibilities: 

• Data and statistical analysis using existing tools and custom scripts/pipelines  
• Create pipelines to process, analyse and integrate multiple omics datasets and cell biology data. 
• Analyse and interpret data 
• Apply statistical approaches 
• Provide guidance to support students who may be assisting with the research 
• Support the team and contribute to the research programme with data-driven decisions 
• Keep an excellent record of analysis 
• Prepare and present data to colleagues and other scientists 
• Contribute to the production of research reports 
• Contribute to collaborative atmosphere of the department. 

The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.  

Skills, knowledge, and experience 

Essential criteria

1.    PhD degree  

2.    Experience with the analysis and the interpretation of high-throughput sequencing data. 

3.    Experience with dataset integration. 

4.    Understanding of both science and informatics. 

5.    Good knowledge of statistics. 

6.    Expert knowledge of a scripting language (Perl or Python) and R. 

7.    Proficiency in working on high-performance Linux clusters (including bash scripting). 

8.    Excellent record keeping and attention to details. 

9.    Self-motivate, enthusiastic and problem-solving attitude. 

10.  Good oral and written communication skills. 

11.  Good presentation skills. 

Desirable criteria 

1.  Experience with handling single-cell datasets and / or integrating different datasets. 

2.  Strong motivation and team spirit. 

3.  General knowledge in biology, strong interest in solving biological questions related to “omics” technologies using computational approaches 

* 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.

Further information

Candidates are strongly encouraged to include a personal statement of previous and future research interests along with the application (CV, List of publications and the contact information of two or more references).