University of Bristol - School of Physiology, Pharmacology and Neuroscience
Senior Research Associate in Machine Learning for Medical Imaging and Molecular
University of Bristol - School of Physiology, Pharmacology and Neuroscience, Bristol, Connecticut, us, 06010
Senior Research Associate in Machine Learning for Medical Imaging and Molecular Prediction
We are seeking a talented and enthusiastic postdoctoral (or working towards a PhD) scientist with experience and a track record in machine learning, particularly for applications of deep learning for medical imaging and/or molecular biomarker development, for a maternity cover role.
The successful applicant will join our world-leading and highly collaborative multi‑disciplinary team of cancer population research scientists at the University of Bristol, based within our Cancer Research UK‑funded Obesity‑related Cancer Epidemiology Programme (OCEP). Our CRUK‑funded programmes (2015–2025) substantially increased understanding of obesity’s importance in cancer aetiology, identifying complex links between the anatomical distribution of adipose tissue, metabolic dysfunction, and cancer risk. We demonstrated an urgent need to go ‘beyond BMI’ to investigate how unhealthy adipose distribution and its metabolic sequelae increase risk of obesity‑related cancers and develop intervention strategies that target those mechanisms.
Hybrid working is available, ideally with at least one day per week on campus; however, this is negotiable.
Responsibilities This role will be based within Work Package 2, ‘Risk stratification’, and will develop multi‑omic models of obesity‑related cancer risk from molecular models of cancer‑related risk factors including imaging‑derived adiposity traits. Risk models will be evaluated for their capacity to inform targeted interventions.
Qualifications
Understanding of molecular epidemiological concepts and population health science
Detailed knowledge of population‑based statistical methods to analyse large, multidimensional datasets
Expertise in the use of machine learning methods for deriving and evaluating predictive models from large datasets, including using deep learning models for extracting features from medical imaging
Experience accessing and analysing large datasets within high‑performance computing and/or cloud compute environments
Strong track record of academic publications
Experience of collaborating and corresponding with multiple studies
Compensation & Work Details Contract type: Fixed Term (maternity cover) between 01/02/2026 – 31/08/2026 or earlier return of the postholder from maternity leave
Work pattern: Full‑time (1 FTE)
Grade: J / Pathway 2
Salary: £43,482 – £50,253 per annum
School/Unit: Bristol Medical School
Shift pattern: 35 hours per week
This advert will close at 23:59 UK time on 04/01/2026.
For informal queries please contact: Dr Paul Yousefi, paul.yousefi@bristol.ac.uk (Senior Research Fellow)
Equal Opportunity The University of Bristol aims to be a place where everyone feels able to be themselves and do their best in an inclusive working environment where all colleagues can thrive and reach their full potential. We want to attract, develop, and retain individuals with different experiences, backgrounds and perspectives – particularly people of colour, LGBT+ and disabled people – because diversity of people and ideas remains integral to our excellence as a global civic institution.
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The successful applicant will join our world-leading and highly collaborative multi‑disciplinary team of cancer population research scientists at the University of Bristol, based within our Cancer Research UK‑funded Obesity‑related Cancer Epidemiology Programme (OCEP). Our CRUK‑funded programmes (2015–2025) substantially increased understanding of obesity’s importance in cancer aetiology, identifying complex links between the anatomical distribution of adipose tissue, metabolic dysfunction, and cancer risk. We demonstrated an urgent need to go ‘beyond BMI’ to investigate how unhealthy adipose distribution and its metabolic sequelae increase risk of obesity‑related cancers and develop intervention strategies that target those mechanisms.
Hybrid working is available, ideally with at least one day per week on campus; however, this is negotiable.
Responsibilities This role will be based within Work Package 2, ‘Risk stratification’, and will develop multi‑omic models of obesity‑related cancer risk from molecular models of cancer‑related risk factors including imaging‑derived adiposity traits. Risk models will be evaluated for their capacity to inform targeted interventions.
Qualifications
Understanding of molecular epidemiological concepts and population health science
Detailed knowledge of population‑based statistical methods to analyse large, multidimensional datasets
Expertise in the use of machine learning methods for deriving and evaluating predictive models from large datasets, including using deep learning models for extracting features from medical imaging
Experience accessing and analysing large datasets within high‑performance computing and/or cloud compute environments
Strong track record of academic publications
Experience of collaborating and corresponding with multiple studies
Compensation & Work Details Contract type: Fixed Term (maternity cover) between 01/02/2026 – 31/08/2026 or earlier return of the postholder from maternity leave
Work pattern: Full‑time (1 FTE)
Grade: J / Pathway 2
Salary: £43,482 – £50,253 per annum
School/Unit: Bristol Medical School
Shift pattern: 35 hours per week
This advert will close at 23:59 UK time on 04/01/2026.
For informal queries please contact: Dr Paul Yousefi, paul.yousefi@bristol.ac.uk (Senior Research Fellow)
Equal Opportunity The University of Bristol aims to be a place where everyone feels able to be themselves and do their best in an inclusive working environment where all colleagues can thrive and reach their full potential. We want to attract, develop, and retain individuals with different experiences, backgrounds and perspectives – particularly people of colour, LGBT+ and disabled people – because diversity of people and ideas remains integral to our excellence as a global civic institution.
#J-18808-Ljbffr