13127 - Postdoctoral Research Associate in Statistical Computing
| Dyddiad hysbysebu: | 18 Medi 2025 |
|---|---|
| Cyflog: | £41,064 i £48,822 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 02 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: | 13127 |
Crynodeb
Grade UE07 £41,064 – £48,822 per annum
CSE / School of Mathematics
Full time: 35 hours per week
Fixed term: from 1st January 2026 until 31st December 2029
The Opportunity:
We are looking for a postdoctoral researcher in statistical computing methods, to work on the next generation of scalable and stable smooth statistical modelling methods, and their implementation in a state of the art python package. This 4 year project is collaborative with industrial partners and Simon Wood (the author of R package mgcv for smooth regression modelling).
The researcher will work with Simon Wood, industrial partners and students on developing statistical computing methods for general smooth regression models in large data – large model settings, and on innovative smoothing parameter and uncertainty quantification methods for general smooth models that approach O(np) computational complexity. As well as conventional academic methods development, a second strand of the work aims to produce the state of the art native python package for general smooth regression modelling, improving on mgcv, incorporating the new methods and providing seamless integration with industrial python based data analysis workflows.
Your skills and attributes for success:
• A PhD in statistical computing methods or closely related field.
• Proven statistical publication track record, with a sound methods component.
• Strong mathematical, numerical and software engineering skills.
• Good knowledge of modern statistical regression, particularly smoothing and mixed models.
• A strong background in Bayesian and frequentist statistical methods.
CSE / School of Mathematics
Full time: 35 hours per week
Fixed term: from 1st January 2026 until 31st December 2029
The Opportunity:
We are looking for a postdoctoral researcher in statistical computing methods, to work on the next generation of scalable and stable smooth statistical modelling methods, and their implementation in a state of the art python package. This 4 year project is collaborative with industrial partners and Simon Wood (the author of R package mgcv for smooth regression modelling).
The researcher will work with Simon Wood, industrial partners and students on developing statistical computing methods for general smooth regression models in large data – large model settings, and on innovative smoothing parameter and uncertainty quantification methods for general smooth models that approach O(np) computational complexity. As well as conventional academic methods development, a second strand of the work aims to produce the state of the art native python package for general smooth regression modelling, improving on mgcv, incorporating the new methods and providing seamless integration with industrial python based data analysis workflows.
Your skills and attributes for success:
• A PhD in statistical computing methods or closely related field.
• Proven statistical publication track record, with a sound methods component.
• Strong mathematical, numerical and software engineering skills.
• Good knowledge of modern statistical regression, particularly smoothing and mixed models.
• A strong background in Bayesian and frequentist statistical methods.