This PhD project aims to develop and apply novel methods for identifying genetic variants causal for human traits and diseases. The primary approach leverages DNA foundational models to improve variant prioritisation, coupled with GPU parallelisation and sub-network isolation to run inference across the entire genome. The project also involves developing methods to quantify information content in datasets (building on nonlinear mixed models literature) and integrating predictions from DNA foundational models into statistical genetics analyses such as polygenic scores and fine-mapping.
DNA Sequence Deep Learning to Map Genome-Wide Genetic Variants Underlying Complex Traits and Disease
Designation
Doctor of Philosophy (PhD)
Key Details
| Field | Details |
|---|---|
| Program | PhD |
| Location | St Lucia, The University of Queensland |
| Research Area | Biological sciences, Engineering, Information and computing sciences, Mathematical sciences |
| Scholarship | Research Project Scholarship |
| Stipend | $37,500 per annum tax-free (2026 rate), indexed annually |
| Tuition | Covered |
| Scholarship Code | TRAITS-YENGO |
| Last Date to Apply | September 25, 2026 |
| Eligibility | Domestic students only |
Research Environment
You will join the Statistical Genomics Laboratory led by Professor Loic Yengo (Snow Fellow) at the Institute for Molecular Bioscience. The lab’s mission is to improve prevention and treatment of common diseases by discovering genes and biological pathways involved in human complex traits. The lab develops scalable analysis tools for large-scale biobank datasets worldwide.
Supervisors:
- Principal Supervisor: Professor Loic Yengo (l.yengo@uq.edu.au)
- Associate Supervisor: Dr Brad Balderson
Eligibility / Qualifications
Your application will be assessed competitively based on academic record, publication record, honours/awards, and employment history. The preferred background includes:
- Academic achievement in bioinformatics, statistical genetics, and/or machine learning
- Working knowledge of genome-wide association studies (GWAS) and Python programming
- Background in PyTorch for neural network architectures and training is highly desirable
- Demonstrated potential for scholastic success
- Domestic students only (not available to international students)
Job Description
As a PhD candidate, you will:
- Develop and train DNA foundational models for genetic variant prioritisation
- Optimise GPU parallelisation and sub-network isolation for genome-wide inference
- Develop new methods to quantify information content in genomic datasets
- Integrate DNA foundational model predictions into polygenic scores and fine-mapping analyses
- Grow an international research profile in statistical genetics
How to Apply
- Check eligibility for the Doctor of Philosophy (PhD) program.
- Prepare documentation as per UQ’s EOI requirements.
- Submit an Expression of Interest (EOI) at apply.uq.edu.au by September 25, 2026.
In your EOI, under the Scholarship/Sponsorship section, enter:
- Are you applying for an advertised project? → Yes
- Project → Research project scholarship
- Scholarship Code → TRAITS-YENGO
- Link to Scholarship Advertisement → Project page URL
For project-specific questions, contact Professor Loic Yengo at l.yengo@uq.edu.au.
