Postdoctoral Research Assistant in Statistical Machine Learning for Genomics
Develop novel statistical and machine learning methods for large-scale genomic, phenotypic, and health data at the University of Oxford.
Best for:Best for quantitative researchers with a strong background in machine learning, statistics, and computational genomics.
Overview
The Department of Statistics at the University of Oxford invites applications for a Postdoctoral Research Assistant to join the Palamara Group. Funded by the European Research Council, the successful candidate will work alongside Professor Pier Palamara to create advanced statistical and machine learning methodologies for studying complex genetic traits and human evolution. The role involves designing scalable algorithms, building robust research software, analyzing biobank-scale datasets, and preparing academic publications. Applicants are expected to have a strong background in statistical inference, machine learning, and computational analysis within a relevant quantitative discipline.
What you'll do
- Develop novel statistical and machine learning methods tailored for large-scale genomic, phenotypic, and health data.
- Implement scalable, reproducible research software to process biobank-scale datasets.
- Rigorously evaluate the statistical properties and predictive performance of newly developed algorithms.
- Prepare manuscripts for peer-reviewed publication and present research findings at scientific meetings.
- Collaborate with other researchers on the project and provide guidance to junior lab members.
Requirements
Degree required: PhD/DPhil in machine learning, statistics, computer science, computational biology, statistical genetics or another relevant quantitative discipline
Language: English
Instruction: English
- Strong foundation in statistical inference and machine learning
- Proven experience in developing novel methods and evaluating their performance
- Strong research software development skills
- Experience handling large-scale or complex quantitative datasets
Funding & terms
How to apply
Deadline: 2026-11-10
Submit your application online through the University of Oxford recruitment portal using the provided vacancy reference number. Ensure that you quote vacancy reference 189277 on all correspondence. Do not attach any manuscripts, papers, transcripts, mark sheets, or certificates as they will not be considered.
- CV
Contact
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