Postdoctoral Research Assistant in Statistical Machine Learning for Genomics

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

Job summary

The Palamara Group at the University of Oxford invites applications for a Postdoctoral Research Assistant in Statistical Machine Learning for Genomics. 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.

You will develop scalable methods, implement reproducible research software and apply them to biobank-scale datasets, prepare publications and collaborate with project

Qualifications

• PhD/DPhil in machine learning, statistics, computer science, computational biology, statistical genetics or another quantitative discipline is expected or near completion.
• Strong foundation in statistical inference and machine learning, with experience developing novel methods and evaluating performance.
• Strong software skills and experience with large-scale or complex quantitative data are essential.

Responsibilities

• Develop novel statistical and machine learning methods for genomic, phenotypic and health data.
• Apply methods to biobank-scale datasets and evaluate statistical properties and predictive performance.
• Prepare publications, present findings and collaborate with project researchers; provide guidance to junior researchers.

Skills

Statistical inference

Machine learning

Research software

Genomics data analysis

Education

PhD or DPhil in a relevant field

Job description

Location

24-29 St Giles’, Oxford, OX1 3LB and BDI Old Road Campus, Oxford OX3 7LF

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).

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.

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.