King's College London

About Us

The laboratory of Dr. Rushad Pavri at King’s College London is inviting applications for a postdoctoral position for a project focused on understanding the molecular mechanisms of antibody somatic hypermutation and its impact on autoimmune disease.

The Pavri group has a long-standing interest and expertise in the molecular mechanisms of antibody maturation and associated genome instability in B lymphocytes. Specifically, we investigate how transcription, chromatin architecture, DNA replication and epigenetic features regulate the key processes of antibody somatic hypermutation and class switch recombination as well as the oncogenic chromosomal translocations arising from these mutagenic pathways. In addition, we study the physiology of germinal centers in murine lymphoid tissues, which is where the antibody maturation reactions occur. The laboratory uses a multi-disciplinary approach including mouse genetics, immunological assays, genetic screening and various genomics tools. A list lab projects and publications from the group can be found here: https://www.kcl.ac.uk/research/pavri-group

About the role

The project is focused on combining artificial intelligence (AI)-based machine learning and experimental validation to decipher the mechanism of somatic hypermutation in antibody variable regions.

Background: Somatic hypermutation (SHM) of immunoglobulin variable (V) genes generates protective antibodies against pathogens and vaccine antigens. SHM also generates pathological autoantibodies causing autoimmune disease and drives lymphomagenesis by mutating proto-oncogenes. Although mutations occur at preferred DNA motifs, there is substantial variation in mutation frequency between motifs, leading to the hypothesis that there exists a sequence "grammar" regulating motif mutability. Our working hypothesis is that this grammar is dictated by intrinsic DNA features flanking the motifs. Deciphering this grammar could provide important insights guiding the clinical modulation of antibody evolution during infection, vaccination and autoimmunity.

Aim: To decipher the sequence grammar underlying SHM, we will use an AI-based machine learning approach to make predictions about genomic features linked with mutability, followed by experimental testing of these hypotheses in human B cells.

The candidate should have a PhD (or have submitted their thesis) in a relevant area of biological or biomedical sciences. They will be expected to design and conduct experiments independently, have excellent written and verbal communication skills, be highly collaborative, co-supervise students and contribute to laboratory management.

The project involves extensive molecular biology, tissue culture and genomics, hence experience in these areas is highly desirable. Knowledge of immunology, gene regulation and flow cytometry will be an advantage.

The project has a major computational component both for AI-driven modelling and predictions, and for bioinformatics analyses of wet-lab data. This will be performed by an excellent team of computational biologists with whom we have a long-standing collaboration. The candidate will have to work closely with the computational team for data analysis and interpretation.

This is a full-time post (35 hours per week), and you will be offered a fixed term contract until 30/04/2028.

About You

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

Essential criteria

1. PhD in biological or biomedical science *
2. Strong publication record, as demonstrated by first author publication(s) in peer reviewed journals (preprints will also be considered)
3. Experience in molecular biology (e.g. cloning), tissue culture and flow cytometry
4. Some knowledge of methods in biochemistry (e.g. protein analyses) and gene regulation
5. Ability to independently design and conduct experiments, analyse data, and present results professionally in written and verbal form
6. Strong collaborative nature and willingness to help with mentoring students
7. A high level of self-motivation and ability to work flexible hours and 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

1. Knowledge of gene regulation, B cell biology and antibody diversification
2. Experience with genomics and next-generation sequencing
3. Basic bioinformatics skills.

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 “Apply Now”. This document will provide information of what criteria will be assessed at each stage of the recruitment process.

Further Information

We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community.

We are committed to working with our staff and unions on these and other issues, to continue to support our people and to develop a diverse and inclusive culture at King's.

As part of this commitment to equality, diversity and inclusion and through this appointment process, it is our aim to develop candidate pools that include applicants from all backgrounds and communities.

We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the advert. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.

To find out how our managers will review your application, please take a look at our ‘How we Recruit’ pages.

This post is subject to Occupational Health clearance.