About this role
Full time - 35 hours per week.
Fixed term contract, if applicable: 01/07/2026 – 31/03/2028 - 21 months.
We are looking for a Post Doctoral Researcher Associate in Biomedical Machine Learning Systems.
The opportunity:
Together we can do great things. Be part of something bigger.
With roles from hospitality to research, there’s a career for everyone at the University of Edinburgh. We can offer opportunities for you to develop in your career and make a real difference in the communities around us while contributing to the world at large.
The University of Edinburgh is a world-class organisation. We look for the best in the field across all disciplines and provide a working environment where academics can develop their careers and passion for their chosen subject area. We offer the full range of academic roles and have a genuine focus on our student’s performance and wellbeing.
Improving diagnosis for rare genetic diseases requires innovative, scalable, and explainable machine learning (ML) systems to interpret complex genomic and clinical data. As part of the Welcome Trust funded PARADIGM project, an initiative aiming to transform genomic medicine by identifying new causes of monogenic disease and developing annotated resources for the scientific community. This role will focus on advancing ML pipelines, frameworks, and tools to enhance variant interpretation, gene-disease models, and clinical decision support.
Your skills and attributes for success:
Essential skills & experience:
PhD (or near completion) in a relevant field (e.g., Machine Learning, Bioinformatics, Computational Biology, or related disciplines). Demonstrated expertise in machine learning, particularly in genomics, rare disease research, or biomedical data analysis. Strong ML programming skills in Python and/or other relevant languages, with experience in optimising and deploying distributed compute tasks. Experience working with high-performance computing environments, including containerised systems (e.g., Docker, Kubernetes) for scalable and reproducible computational workflows. Ability to work collaboratively within interdisciplinary teams (academics, clinicians, curators, software engineers) and communicate complex technical concepts clearly. Experience with scientific writing, including the ability to publish in peer-reviewed journals and present research findings at conferences.