Postdoctoral Research Assistant in Statistical Machine Learning for Genomics

University-of-Oxford; Oxford; On-site; GBP 39,000 - 53,000; Full time

Job summary

The University of Oxford invites applications for a Postdoctoral Research Assistant in Statistical Machine Learning for Genomics to join the Palamara Group, funded by the ERC. You will develop scalable statistical and ML methods for large-scale genomic and health data and contribute to high-impact publications.

Applicants should hold or be close to completing a PhD in a quantitative field, with strong ML and statistics foundations and experience with reproducible research software.

Qualifications

• PhD/DPhil in a quantitative discipline (ML, statistics, CS, computational biology, statistical genetics) or related field.
• Strong foundation in statistical inference and machine learning.
• Experience with large-scale or complex data and research software.

Responsibilities

• Develop scalable ML/statistical methods for genomic data.
• Implement reproducible research software and apply to biobank-scale datasets.
• Prepare publications, present findings, collaborate with project researchers and mentor junior researchers.

Skills

Statistical ML

Genomics

Large-scale data

Reproducible research

Education

PhD/near completion in quantitative field (ML/Stats/CS/Genomics)

Tools

Python

R

Git

Job description

Postdoctoral Research Assistant in Statistical Machine Learning for Genomics

Statistics, 24-29 St Giles', Oxford, OX1 3LB

We invite applications for a Postdoctoral Research Assistant in Statistical Machine Learning for Genomics to join the Palamara Group at the University of Oxford, funded by the European Research Council (ERC). Working with Professor Pier Palamara, you will develop novel statistical and machine learning methods for large-scale genomic, phenotypic and health data to address questions in human genetics and evolution.

The research programme brings together statistical inference, machine learning and AI to study the genetic basis of complex traits and how evolution shapes their genetic architectures. Possible directions include genomic language models, multimodal learning integrating genomic and health data with functional, genealogical and evolutionary information, federated learning, and multi-trait and multi-ancestry methods. Applications include association testing, fine-mapping and polygenic prediction.

You will develop scalable methods, implement reproducible research software and apply them to biobank-scale datasets, rigorously evaluating their statistical properties and predictive performance. You will prepare publications, present findings, collaborate with project researchers and provide guidance to junior researchers.

You should hold, or be close to completing, a PhD/DPhil in machine learning, statistics, computer science, computational biology, statistical genetics or another relevant quantitative discipline. You should have a strong foundation in statistical inference and machine learning, with experience developing novel methods and evaluating their statistical and empirical performance. Strong research software skills and experience with large-scale or complex quantitative data are also essential. Previous experience in genomics or evolutionary biology is desirable but not essential.

For informal enquiries, please contact Professor Pier Palamara at [e-mail]. Further information about the group is available at https://palamaralab.github.io/.

This position is offered full time on a fixed term contract for 36 months and is funded by the European Research Council. Only applications received before 12 midday (UK time) on 10 November 2026 will be considered.

Salary (£) : Research Grade 7: salary in range £39,424 - £47,779 with a discretionary range to £53,483 p.a. (pro rata). This is inclusive of a pensionable Oxford University Weighting of £1,730 per year (pro rata).