13146 - Research Associate
| Dyddiad hysbysebu: | 25 Medi 2025 |
|---|---|
| Cyflog: | £41,064 i £48,822 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 16 Hydref 2025 |
| Lleoliad: | Edinburgh, Scotland |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 3 ddiwrnod yr wythnos |
| Cwmni: | University of Edinburgh |
| Math o swydd: | Cytundeb |
| Cyfeirnod swydd: | 13146 |
Crynodeb
Grade UE07: £41,064 - £48,822 per annum (pro-rata if part time)
CSE / School of Informatics
Full-time: 35 hours per week
Fixed term: 12 months
The Opportunity:
The successful candidate will contribute to an industry-funded project focused on using compilation techniques to improve the performance of inference over sparse neural networks. You will be expected to lead the research on certain aspects of the project while contributing to rest of the project (assisting in research student supervision, preparing presentations and reports for deliverables, developing related/follow-on projects).
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 building systems for machine learning, performance tuning, and building optimizing compilers are 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, 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.
Please include the following documents in your application:
CV
Cover letter
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.
Preferably, experience in building systems for machine learning.
Preferably, experience in building optimizing compilers.
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.
CSE / School of Informatics
Full-time: 35 hours per week
Fixed term: 12 months
The Opportunity:
The successful candidate will contribute to an industry-funded project focused on using compilation techniques to improve the performance of inference over sparse neural networks. You will be expected to lead the research on certain aspects of the project while contributing to rest of the project (assisting in research student supervision, preparing presentations and reports for deliverables, developing related/follow-on projects).
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 building systems for machine learning, performance tuning, and building optimizing compilers are 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, 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.
Please include the following documents in your application:
CV
Cover letter
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.
Preferably, experience in building systems for machine learning.
Preferably, experience in building optimizing compilers.
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.