Research Associate / Research Fellow (Cancer Sciences / Digital Pathology / AI)

RFCSR; Manchester; On-site; GBP 36,000 - 56,000; Full time

Job summary

The University of Manchester is seeking a Research Associate or Research Fellow to contribute to an ambitious cancer sciences programme, focusing on digital pathology, AI, and data-driven discovery. You will develop computational methods to analyze large-scale imaging and clinical data, working with clinicians and researchers in a collaborative setting.

Responsibilities include designing analytical pipelines, integrating datasets, and interpreting results to improve cancer diagnosis, prognosis,

Qualifications

• PhD (or near completion) in computational biology, bioinformatics, data science, computer science, or a closely related discipline.
• For Research Fellow level: significant postdoctoral research experience and a strong publication record.

Responsibilities

• Design and implement analytical pipelines for cancer imaging and omics datasets.
• Integrate diverse datasets and contribute to study design.
• Interpret results in a clinically meaningful context and disseminate findings through publications and presentations.

Skills

Machine learning

Python

R

Data analysis

Statistical modelling

Education

PhD or near completion

Tools

Python

Job description

Research Associate / Research Fellow (Cancer Sciences / Digital Pathology / AI)

The University of Manchester
Manchester, United Kingdom

General Description
The University of Manchester is seeking to appoint a Research Associate or Research Fellow to contribute to an ambitious programme of research within cancer sciences, with a strong focus on digital pathology, artificial intelligence, and data-driven biomedical discovery. The successful candidate will join a dynamic, interdisciplinary research environment and work closely with clinicians, computational scientists, and translational researchers.

The role involves the development and application of advanced computational and statistical methods to analyse complex biomedical datasets, including large-scale imaging and clinical data. The post holder will contribute to ongoing research projects aimed at improving cancer diagnosis, prognosis, and treatment through innovative use of machine learning and AI techniques.

Key responsibilities include designing and implementing analytical pipelines, integrating diverse datasets, contributing to study design, and interpreting results in a clinically meaningful context. The candidate will also support dissemination of findings through high-impact publications, presentations, and collaborative engagement with academic and healthcare partners.

The position offers opportunities to engage in cutting-edge research within a leading research-intensive university, contributing to impactful scientific advancements and improving patient outcomes. The role may be appointed at Research Associate (Grade 6) or Research Fellow (Grade 7) level depending on experience. This is a full-time, fixed-term appointment.

Eligibility Criteria

• PhD (or near completion) in computational biology, bioinformatics, data science, computer science, or a closely related discipline
• For Research Fellow level: significant postdoctoral research experience and a strong publication record

Required Expertise/Skills

• Experience in machine learning, artificial intelligence, or statistical modelling applied to biomedical or imaging data
• Strong programming skills (e.g., Python, R, or similar)
• Experience working with large-scale datasets, including medical imaging or omics data
• Ability to develop and implement reproducible analytical workflows
• Strong analytical, problem-solving, and data interpretation skills
• Excellent written and verbal communication skills
• Ability to work collaboratively in multidisciplinary research teams
• Experience in digital pathology or healthcare data is desirable

Salary Details
Research Associate (Grade 6): £36,024 – £44,263 per annum
Research Fellow (Grade 7): £45,585 – £56,021 per annum (depending on experience)

Application Deadline
Not explicitly specified