Postdoctoral Research Assistant in Statistical Machine Learning for Genomics

Economics Network; Oxford; Hybrid; GBP 39,000 - 48,000; Full time

Job summary

Palamara Group at the University of Oxford seeks a Postdoctoral Research Assistant in Statistical Machine Learning for Genomics. You will develop novel statistical and machine learning methods for large-scale genomic and health data, address questions in human genetics and evolution, and implement reproducible research software.

The role requires a PhD/DPhil in a quantitative field, strong ML and statistical inference, and experience with large datasets. Applicants work with Prof.

Qualifications

• PhD/DPhil in machine learning, statistics, computer science, genomics or related field.
• Strong foundation in statistical inference and machine learning.
• Experience developing novel methods and evaluating their performance.
• Strong software skills and handling large-scale data.

Responsibilities

• Develop scalable statistical and ML methods for genomics data.
• Implement reproducible software and apply methods to biobank-scale datasets.
• Prepare publications and present findings.
• Collaborate with researchers and mentor junior staff.

Skills

Statistical inference

Machine learning

Research software

Large-scale data

Genomics

Education

PhD/DPhil

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.

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.