Jobs

Machine Learning Software Developer


Job details
  • The University of Edinburgh
  • Midlothian
  • 4 months ago
Applications closed

Grade UE06 - £32,982 - £38,205 per annum

College of Medicine & Veterinary Medicine - Clinical Sciences

Full time - 35 hours per week

Fixed term contract to 28/02/2027

The Opportunity:

Founded in 2024, the Learning Model of Life is a cross-college initiative of The University of Edinburgh. We are a constituent of the University’s Generative AI Lab (GAIL) and are supported by EPCC, the UK’s leading centre of Supercomputing and Data Science. 

We are now looking for an enthusiastic Software Developer to support this ground breaking program of research. We are a constituent of the University's Generative AI Lab (GAIL) and are supported by EPCC, the UK's leading centre of Supercomputing and Data Science. You'll be instrumental in building and optimising automated tools that handle diverse clinical and biological datasets, which are crucial for the design, development, and training of advanced AI models. Your work will directly contribute to the future of biological science and genetic research. You will work in a vibrant academic environment at the University of Edinburgh, surrounded by experts in biology, medicine, computing, and machine learning. You'll have freedom to innovate, support to grow, and the opportunity to contribute to research that will transform the future of healthcare and beyond.

This post is full-time (35 hours per week), however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working. 

Your skills and attributes for success: 

Relevant undergraduate degree, in a relevant discipline (e.g., artificial intelligence, computer science, software engineering). Data cleaning and curation in health data Experience and skill in data analyses.  Understanding of the principles of security and confidentiality of personal data in a research context. Ability to thrive in a rapidly changing environment with high ambiguity; self-starter mentality

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