Overview

The School of Informatics at the University of Edinburgh invites applications for a research associate (post-doc) position in the area of large-scale analytics on emerging hardware.

The Opportunity:

The successful candidate will contribute to an industry-funded project focused on using CXL-based disaggregated memory to improve the performance of large-scale data analytics. This is an exciting opportunity to get involved in an emerging technology (CXL and memory disaggregation) with significant potential for both short- and long-term impact. Key tasks will entail characterizing a state-of-the-art CXL hardware platform, bringing up and tuning analytics workloads, identifying and evaluating relevant performance optimizations at software and hardware layers, as well as writing up and presenting findings.

Candidates must have a PhD (or nearing completion) in Computer Science or related field and a strong research track record demonstrated through publications at top-tier venues. Experience with performance characterization, workload tuning, and/or system software and databases/analytics highly desirable. 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.

This post is advertised as 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:

• A PhD (or near completion of PhD) in Computer Science or related field.
• Excellent research track record as demonstrated through publications at top-tier conferences and/or high-impact journals.
• Experience in working in one or more of following areas: hardware benchmarking; performance characterization; workload tuning and bottleneck identification; databases/analytics software; operating systems.
• Ability to communicate complex information clearly, both orally and in writing.
• Possess high level of initiative, be detail oriented and ability to effectively work in a team setting.
• Preferably, experience in research student supervision and grant writing.