Role: Software Developer (Machine Learning for ALS Diagnosis)
Grade and Salary: Grade 6 - £31,236 - £36,636
Contract Type: Full Time (1FTE), Fixed Term (until 27/02/2026)
Location: Edinburgh Campus
Purpose of the Role
The Software Developer will contribute to the development, validation, and deployment of a deep learning-based diagnostic tool for Amyotrophic Lateral Sclerosis (ALS). The role is central to the EPSRC Impact Accelerator Award (IAA) project, which aims to translate machine learning research into a functional and interactive platform for clinical and industry use. The successful candidate will be responsible for refining deep learning models, conducting usability testing, and supporting stakeholder engagement activities, including presentations at events and conferences.
Detailed Description
The role involves working with deep learning models trained on autopsy brain images, assisting in the integration of machine learning outputs into a front-end application, and ensuring the tool meets usability and clinical translation requirements. The candidate will also contribute to industry engagement efforts by preparing demonstrations and presentations showcasing the platform’s potential for ALS diagnostics and cognitive impairment stratification.
The position is well-suited for a researcher with a background in machine learning, medical imaging, or computational neuroscience, who is interested in the practical application of AI in healthcare. The candidate will have access to state-of-the-art resources and will collaborate closely with clinicians, industry partners, and research teams at Heriot-Watt University and the University of Aberdeen.
Key Duties and Responsibilities
Machine Learning Model Development & Optimisation
1. Assist in refining and optimising the deep learning models.
2. Evaluate model performance using metrics such as accuracy, sensitivity, specificity, and Grad-CAM visualisations.
3. Implement feature enhancements, including explainability tools.
Software Development & Front-End Integration
4. Work on integrating machine learning outputs into a functional web-based and/or App diagnostic application.
5. Assist in developing visualisations, heatmaps, and clinical decision-support tools.
Usability Testing & Validation
6. Conduct task-based usability testing with clinicians and researchers.
7. Collect and analyse user feedback to refine the tool’s design and functionality.
Industry Engagement & Commercialisation Support
8. Assist in preparing demonstrations, outreach materials, and presentations for industry and clinical stakeholders.
9. Represent the project at key conferences and industry events.
10. Support the development of marketing materials and video demonstrations.
Education, Qualifications & Experience
Essential Criteria
11. Master’s degree (or equivalent experience) in Machine Learning, Computer Science, Medical Imaging, Biomedical Engineering, or a related field.
12. Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and transfer learning models.
13. Proficiency in Python programming and handling medical image datasets.
14. Strong understanding of computer vision, CNNs, and attention mechanisms.
15. Experience with usability testing and user interface integration for machine learning applications.
16. Excellent communication and presentation skills, with the ability to engage with clinicians and industry professionals.
Desirable Criteria
17. Experience working with neuroimaging datasets or histopathology images.
18. Knowledge of Grad-CAM or other explainability methods for deep learning.
19. Familiarity with front-end web development (e.g., React, Flask, or Django) for integrating machine learning tools.
20. Prior experience in technology commercialisation, regulatory frameworks, or MedTech industry engagement.
21. Track record of conference presentations, scientific writing, or industry outreach.