10569- Research Fellow (Machine Learning and Data Science)
| Dyddiad hysbysebu: | 23 May 2024 |
|---|---|
| Cyflog: | £39,347.00 to £46,974.00 per year |
| Oriau: | Full time |
| Dyddiad cau: | 20 June 2024 |
| Lleoliad: | Edinburgh, Scotland |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 4 ddiwrnod yr wythnos |
| Cwmni: | University of Edinburgh |
| Math o swydd: | Temporary |
| Cyfeirnod swydd: | 10569 |
Crynodeb
Research Fellow (Machine Learning and Data Science)
UE07 - £39,347 - £46,974
CMVM / MGPHS / USHER Institute
Full time (35hrs per week)
Fixed Term available from 1 October 2024 to 30 September 2028
Location: Usher Institute, Usher Building Edinburgh Bioquarter
The Centre for Medical Informatics at the Usher Institute within The University of Edinburgh is looking for a postdoctoral researcher with experience in data science and machine learning to support the application of advanced statistical learning methods across the newly-established UKRI Hub for Metabolic Psychiatry.
The Opportunity:
The researcher will have the opportunity to contribute to the design and implementation of advanced machine learning and data science and statistical methods, including, Mendelian randomisation, latent class analysis, time series analysis, and structural equation modelling in the UKRI Hub for Metabolic Psychiatry. This role is highly collaborative, involving engagement with co-investigators, collaborators and partners to ensure delivery of the Hub for Metabolic Psychiatry’s multidisciplinary programme of work and open science platform. The post-holder will interact with researchers from various disciplines at the University of Edinburgh, Kings College London, University of Bristol, University of Exeter, and other project partners.
Informal enquiries may be directed to Saturnino Luz, Professor, Chair of Digital Biomarkers & Precision Medicine (s.luz@ed.ac.uk)
Your skills and attributes for success:
• PhD (or near completion) in Computer Science, Statistics, Bioinformatics, Machine Learning or related field, or considerable relevant and equivalent experience.
• Strong data science skills, with experience in managing and processing heterogeneous, time-based data sets.
• Experience in designing and employing statistical and machine learning methods for the analysis biological data.
• Track record in designing and implementing data curation and sharing platforms.
• Excellent programming skills.
UE07 - £39,347 - £46,974
CMVM / MGPHS / USHER Institute
Full time (35hrs per week)
Fixed Term available from 1 October 2024 to 30 September 2028
Location: Usher Institute, Usher Building Edinburgh Bioquarter
The Centre for Medical Informatics at the Usher Institute within The University of Edinburgh is looking for a postdoctoral researcher with experience in data science and machine learning to support the application of advanced statistical learning methods across the newly-established UKRI Hub for Metabolic Psychiatry.
The Opportunity:
The researcher will have the opportunity to contribute to the design and implementation of advanced machine learning and data science and statistical methods, including, Mendelian randomisation, latent class analysis, time series analysis, and structural equation modelling in the UKRI Hub for Metabolic Psychiatry. This role is highly collaborative, involving engagement with co-investigators, collaborators and partners to ensure delivery of the Hub for Metabolic Psychiatry’s multidisciplinary programme of work and open science platform. The post-holder will interact with researchers from various disciplines at the University of Edinburgh, Kings College London, University of Bristol, University of Exeter, and other project partners.
Informal enquiries may be directed to Saturnino Luz, Professor, Chair of Digital Biomarkers & Precision Medicine (s.luz@ed.ac.uk)
Your skills and attributes for success:
• PhD (or near completion) in Computer Science, Statistics, Bioinformatics, Machine Learning or related field, or considerable relevant and equivalent experience.
• Strong data science skills, with experience in managing and processing heterogeneous, time-based data sets.
• Experience in designing and employing statistical and machine learning methods for the analysis biological data.
• Track record in designing and implementing data curation and sharing platforms.
• Excellent programming skills.