Research Assistant

The Opportunity:

Don’t just see the bigger picture, help create it

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 students performance and wellbeing.

The School of Informatics, University of Edinburgh invites applications for a Research Associate to work on an ARIA TA3 project “Breaking Down the Compute Graph Step by Step: A scalable and modular simulation”. This project, co-led by teams from the University of Edinburgh, Imperial College London, and the University of Cambridge, focuses on the development of a scalable and modular performance simulation framework for future systems.

Candidates must have an undergraduate degree or M.Sc. in Computer Science or related field and a strong research track record demonstrated through publications at top-tier venues. We are looking for a highly motivated candidate with strong initiative and commitment to excellence, and an ability to conduct world-class research in a team setting.

Your skills and attributes for success:

Essential

• Strong background and expertise in computer systems, distributed systems, computer architectures, machine learning systems.
• Experience and evidence of effective independent research work within an interdisciplinary team. More broadly, demonstrated ability to lead, design and complete research projects, to solve problems independently and make original contributions to research.
• Excellence in written and oral communication, analytical, planning, and time management skills.
• Publications in top computer system venues, such as OSDI, SOSP, NSDI, ASPLOS and related conferences.

Desirable

• A master or in final stage of a PhD in computer science or a related discipline, with excellent technical expertise and experience in the areas of Data Science and Informatics as defined by the Bayes Centre and Huawei.