ACAD108415

University of Bristol; Bristol; Hybrid; GBP 35,000 - 45,000; Full time

Job summary

A leading research institution is looking for a talented postdoctoral scientist or PhD candidate to join its multi-disciplinary team focused on cancer research. The role involves leveraging machine learning, particularly deep learning for medical imaging, to study the links between obesity and cancer. The position offers hybrid working arrangements, emphasizing collaboration and data analysis skills, with an expectation of significant contributions to academic publications.

Qualifications

• Experience in machine learning applications, especially in medical imaging.
• Proven track record of academic publications.
• Ability to analyze large multidimensional datasets.

Responsibilities

• Join a multi-disciplinary team in cancer population research.
• Investigate links between adipose tissue distribution and cancer risk.
• Develop intervention strategies targeting obesity-related mechanisms.

Skills

Machine learning

Deep learning for medical imaging

Population health science

Statistical methods

High-performance computing

Education

PhD or working towards a PhD

Job description

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 previously 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.

• 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