cfDNA Postdoctoral Fellow / Senior Postdoctoral Fellow in statistical genomics

Wellcome Genome Campus

Hinxton

Hybrid

GBP 39,000 - 50,000

Full time

5 days ago

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Job summary

Wellcome Sanger Institute is seeking a Postdoctoral Fellow or Senior Postdoctoral Fellow focused on cell-free DNA (cfDNA) from pregnant women and people. You will develop statistical and deep learning methods to decode fetal/m maternal genotypes from cfDNA and explore biomarkers for pregnancy complications using methylation-aware genome data.

You will collaborate with international partners, contribute to method development, and mentor junior researchers in a hybrid working environment on the

Qualifications

PhD in statistics, statistical genetics, genetics, genomics, bioinformatics, machine learning or similar.
Strong background in statistics or statistical genetics with ability to develop, implement and benchmark new statistical methods
Experience working with raw sequence data (alignments, variant calling, quality control)
Experience analysing large-scale human genetic or genomic data
Full working proficiency in Python and/or R, and Unix/Linux
Experience or enthusiasm for using AI tools to improving productivity in scientific research, statistical methods development and/or software development
Knowledge and understanding of statistical methods appropriate for large-scale genetic research
Previous experience writing highly reproducible code with github, Snakemake/Nextflow, RMarkdown or similar
Experience developing pipelines or software for large-scale analyses of genomic data, including running workflows efficiently on HPC and/or cloud infrastructure
Creativity and original scientific ideas
Enthusiasm and commitment
Attention to detail
Effective problem-solving skills
Ability to communicate ideas and results effectively both verbally and in writing
Ability to prioritise, work independently and organise own workload
Ability to collaborate effectively with an interdisciplinary team and collaborators

Responsibilities

Develop statistical and deep learning methods for calling fetal and maternal genotypes from high-coverage sequence data on cfDNA, and for discovering new Mendelian disease genes using the resulting data. These methods will then be applied to study pregnancy complications in large samples.
Develop biomarkers for pregnancy complications from methylation-aware whole-genome sequence data on cfDNA, including methods development to incorporate both epigenetic and genetic information in the same assay. You will have the opportunity to collaborate with groups at the Norwegian Institute of Public Health (Siri Eldevik Håberg), the University of Helsinki (Simone Rubinacci) and elsewhere.

Skills

PhD in statistics/genetics

Statistics background

Raw sequence data

Large-scale genomic data

Python/R

Unix/Linux

AI tools in research

Statistical methods

Reproducible code

Workflow/Pipeline dev

Education

PhD in statistics/genetics

Tools

GitHub

Snakemake

Nextflow

RMarkdown

Job description

About the group:

The Martin group works on the genetic basis of rare and common disease, using large-scale human genetic datasets with rich linkage to electronic health records and questionnaires. Over the last few years, we have had a particular emphasis on the genetics of neurodevelopmental disorders and related traits, the genetics of complex disease in populations with high levels of consanguinity, and factors influencing germline mutation rates.

The group is now opening new research directions involving genomics and multi-omics in very early life. Most conditions in the earliest stages of life remain untouched by large-scale genomics, and molecular predictors that could guide clinical management are urgently needed. We are building a programme spanning a clinical continuum from pregnancy loss, through fetal growth restriction, to complications of premature birth. This includes large-scale sequencing of cell-free DNA (cfDNA) from pregnant women and people to identify monogenic causes of pregnancy loss and identify biomarkers for fetal growth restriction, as well as genetic and multi-omic studies of neonatal conditions using nationwide dried blood spots.

What you'll be doing:

This particular role is focused on cell-free DNA (cfDNA) from pregnant women and people. cfDNA in maternal plasma is a mixture of maternal and fetal (placental) molecules, and offers a route to studying fetal genomes and placental biology at a scale that is impossible with fetal tissue. Possible projects include:

Developing new statistical and deep learning methods for calling fetal and maternal genotypes from high-coverage sequence data on cfDNA, and for discovering new Mendelian disease genes using the resulting data. These methods will then be applied to study pregnancy complications in large samples.

Developing biomarkers for pregnancy complications from methylation‑aware whole‑genome sequence data on cfDNA, including methods development to incorporate both epigenetic and genetic information in the same assay. You will have the opportunity to collaborate with groups at the Norwegian Institute of Public Health (Siri Eldevik Håberg), the University of Helsinki (Simone Rubinacci) and elsewhere.

About you:

We are recruiting at two levels: Postdoctoral Fellow and Senior Postdoctoral Fellow. In both cases, you will have a demonstrable background in statistics or statistical genetics. Experience of working with cfDNA is highly desirable but not essential. The role would particularly suit if you have previously worked on methods for somatic variant calling, or for phasing and imputation from low‑coverage sequence data. You would also bring excellent software development skills, since the methods developed here are intended to be applied across several large cohorts and shared with collaborators. The Senior Postdoctoral Fellow role is intended for someone with particularly strong experience in these areas who is ready to work with a high degree of independence, to shape the direction of the methods development, to lead collaborations and to mentor more junior members of the group. You will be considered at the level that best matches your experience, so please apply even if you are unsure which position fits. Prospective candidates are welcome to contact Hilary Martin (hcm@sanger.ac.uk) with informal queries before deciding whether to apply.

Essential Skills:

PhD in a relevant area (statistics, statistical genetics, genetics, genomics, bioinformatics, machine learning or similar)

Strong background in statistics or statistical genetics, including the ability to develop, implement and benchmark new statistical methods

Experience working with raw sequence data (alignments, variant calling, quality control)

Experience analysing large‑scale human genetic or genomic data

Full working proficiency in Python and/or R, and Unix/Linux

Experience or enthusiasm for using AI tools to improving productivity in scientific research, statistical methods development and/or software development

Knowledge and understanding of statistical methods appropriate for large‑scale genetic research

Previous experience writing highly reproducible code with github, Snakemake/Nextflow, RMarkdown or similar

Experience developing pipelines or software for large‑scale analyses of genomic data, including running workflows efficiently on HPC and/or cloud infrastructure

Creativity and original scientific ideas

Enthusiasm and commitment

Attention to detail

Effective problem‑solving skills

Ability to communicate ideas and results effectively both verbally and in writing

Ability to prioritise, work independently and organise own workload

Ability to collaborate effectively with an interdisciplinary team and collaborators

Senior Postdoctoral Fellow additional skills:

Completion of a previous postdoctoral position, or other highly relevant training or experience

Experience of developing methods for variant calling from sequence data, or for phasing and imputation from low‑coverage sequence data

Evidence of substantial research contributions and intellectual leadership, demonstrated through publications, methods, software or other relevant outputs, appropriate to career stage and opportunities.

Specialist knowledge and understanding to drive the project from the outset

Demonstrated ability to lead collaborations and to supervise or mentor junior researchers

You will join our supportive and collaborative Postdoctoral Fellow community, with access to training, learning and networking across the Institute.

Hybrid Working at Wellcome Sanger:

We recognise that there are many benefits to Hybrid Working; including an improved work‑life balance, with more focused time, as well as the ability to organise working time so that collaborative opportunities and team discussions are facilitated on campus. The hybrid working arrangement will vary for different roles and teams. The nature of your role and the type of work you do will determine if a hybrid working arrangement is possible.

Equality, Diversity and Inclusion:

We aim to attract, recruit, retain and develop talent from the widest possible talent pool, thereby gaining insight and access to different markets to generate a greater impact on the world. We have a supportive culture with the following staff networks: LGBTQ+, Parents and Carers, Disability, Gender Equity and Race Equity to bring people together to share experiences, offer specific support and development opportunities and raise awareness. The networks are also a place for allies to provide support to others.

We believe people do their best work when they can be their authentic selves. That’s why we’re committed to creating a truly inclusive culture at Sanger Institute. We will consider all individuals without discrimination and are committed to creating an inclusive environment for all employees, where everyone can thrive.

Our Benefits:

We are proud to deliver an awarding campus‑wide employee wellbeing strategy and programme.

The importance of good health and adopting a healthier lifestyle and the commitment to reduce work‑related stress is strongly acknowledged and recognised at Sanger Institute.