At a Glance
- Tasks: Advance machine learning systems for genomic medicine and collaborate with diverse teams.
- Company: Join the University of Edinburgh, a world-class institution dedicated to innovation.
- Benefits: Competitive salary, professional development, and a chance to make a real impact.
- Other info: Dynamic, interdisciplinary environment with excellent career growth opportunities.
- Why this job: Shape the future of healthcare by working on cutting-edge research in genomics.
- Qualifications: PhD or near completion in relevant fields and strong machine learning skills required.
The predicted salary is between 41064 - 48822 £ per year.
UE07: £41,064.00 - £48,822.00 Per Annum. College of Science and Engineering / School of Informatics / Institute for Machine Learning. Full Time - 35 Hours per week. Fixed Term Contract, if applicable: 01/07/2026 – 31/03/2028 - 21 months.
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.
The successful candidate will collaborate with academics, software engineers, curators, clinical scientists, clinicians, and patient groups to design FAIR (Findable, Accessible, Interoperable, Reusable) and resource‑efficient ML solutions for real‑world deployment. Key responsibilities include:
- Developing standards‑compliant, reusable modelling frameworks for rare genetic disease knowledge representation from multi‑modal data.
- Creating scalable and privacy‑preserving computing solutions for genetic and health data.
- Integrating novel ML technologies into the broader PARADIGM project workflow.
- Proactively disseminating work through national and international collaborations.
- Ensuring explainable AI/ML for clinical use.
- Supporting students through mentorship and knowledge‑sharing activities.
This position offers the opportunity to contribute to cutting‑edge research, work in the Biomedical Informatics Group, a multidisciplinary team in the School of Informatics, and shape the future of genomic medicine.
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.
Desirable Skills & Experience
- Experience with API development and integration, including RESTful APIs or other standards for data exchange and interoperability.
- Familiarity with Model Configuration Protocols (MCP) for defining and deploying machine learning models in reproducible, scalable workflows.
- Experience with database systems (e.g., SQL/NoSQL) and knowledge graph construction (e.g., Neo4j, RDF triples) for organising and querying complex biomedical data.
- Familiarity with tools for data integration, transformation, or workflow orchestration in large‑scale projects.
- Familiarity with ontologies (e.g., HPO, OMIM) and semantic technologies for knowledge graph construction.
- Experience with the use of GNNs in a multi‑modal data integration setting (for example using fusion, hierarchical modelling, contrastive learning).
- Experience with variant‑phenotype mapping, gene‑disease model creation, or computational phenomics.
- Experience with explainable AI/ML and open‑source software development.
- Experience mentoring students (undergraduate, MSc, or PGR) or contributing to educational initiatives.
14237 - Research Associate employer: University of Edinburgh
The University of Edinburgh is an exceptional employer, offering a vibrant work culture that fosters innovation and collaboration within the School of Informatics. As a Research Associate, you will have the opportunity to contribute to groundbreaking research in genomic medicine while benefiting from a supportive environment that prioritises professional development and mentorship. With access to world-class resources and a commitment to making a meaningful impact in the community, this role is perfect for those looking to advance their careers in a prestigious academic setting.
StudySmarter Expert Advice🤫
We think this is how you could land 14237 - Research Associate
✨Tip Number 1
Network like a pro! Reach out to people in your field, especially those connected to the University of Edinburgh. Attend events, join online forums, and don’t be shy about asking for informational interviews. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your machine learning projects, especially those related to genomics or biomedical data. This gives potential employers a tangible look at what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to research roles. Think about how you would explain complex concepts in simple terms, as collaboration is key in this field. We want to see how you communicate with both technical and non-technical folks!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, keep an eye on our career page for any new opportunities that pop up. We’re always looking for passionate individuals to join our team!
We think you need these skills to ace 14237 - Research Associate
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Research Associate role. Highlight your relevant skills in machine learning and bioinformatics, and show how your experience aligns with the key responsibilities mentioned in the job description.
Showcase Your Collaboration Skills:Since this role involves working with a diverse team, emphasise your ability to collaborate with academics, clinicians, and software engineers. Share examples of past projects where teamwork was crucial to success.
Demonstrate Your Technical Expertise:Be specific about your programming skills, especially in Python and any experience with high-performance computing environments. Mention any relevant projects or research that showcase your expertise in machine learning and genomic data analysis.
Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way to ensure your application gets the attention it deserves. Plus, it’s super easy to do!
How to prepare for a job interview at University of Edinburgh
✨Know Your Stuff
Make sure you brush up on your machine learning concepts, especially as they relate to genomics and rare diseases. Be ready to discuss your PhD work and how it connects to the role. This shows you're not just knowledgeable but also genuinely interested in the field.
✨Showcase Your Collaboration Skills
Since this role involves working with a diverse team, be prepared to share examples of past collaborations. Highlight how you've communicated complex ideas to non-technical colleagues or worked with clinicians. This will demonstrate your ability to thrive in an interdisciplinary environment.
✨Demonstrate Your Technical Prowess
Be ready to talk about your programming skills, particularly in Python. If you've worked with Docker or Kubernetes, mention specific projects where you optimised workflows. This will help the interviewers see your practical experience in action.
✨Prepare for Questions on Explainability
Given the focus on explainable AI/ML, think about how you can articulate the importance of transparency in machine learning models. Prepare to discuss any relevant experiences or projects where you ensured that your models were interpretable and user-friendly.
