Postdoctoral Research Assistant in Artificial Intelligence and Digital Pathology

RFCSR; Greater London; Sur place; GBP 37 000 - 44 000; Plein temps

Résumé du poste

Queen Mary University of London invites applications for a Postdoctoral Research Assistant in Artificial Intelligence and Digital Pathology. The role focuses on developing AI-driven tools for biomedical imaging and pathology data, with responsibilities across independent and collaborative research, algorithm development, and dissemination.

The successful candidate will join a multidisciplinary team, contribute to translational research, and work in a leading academic environment with

Qualifications

• PhD in Computer Science, Artificial Intelligence, Biomedical Engineering or related field.
• Experience in ML, DL or CV.
• Experience with medical imaging or digital pathology data.
• Strong programming background and handling large datasets.
• Independent and collaborative research ability.
• Evidence of publications or research outputs.

Responsabilités

• Conduct independent and collaborative research.
• Develop novel algorithms and computational pipelines.
• Prepare research outputs for publication in high-impact journals.
• Support grant-related activities.
• Engage in academic dissemination through conferences and workshops.

Connaissances

Python

Machine learning

Deep learning

Computer vision

Large datasets handling

Formation

PhD (or near completion)

Outils

TensorFlow

PyTorch

Description du poste

Postdoctoral Research Assistant in Artificial Intelligence and Digital Pathology

General Description:
Queen Mary University of London is seeking to appoint a Postdoctoral Research Assistant to contribute to pioneering research at the intersection of artificial intelligence and digital pathology. The position is based within a leading academic research environment and supports ongoing projects focused on applying advanced computational methods to biomedical imaging and pathology data.

The successful candidate will work as part of a multidisciplinary team involving clinicians, data scientists, and researchers, contributing to the development and application of AI-driven tools for the analysis of complex medical datasets. The role includes designing and implementing machine learning models, processing large-scale pathology imaging data, and contributing to translational research aimed at improving diagnostic and clinical outcomes.

Responsibilities include conducting independent and collaborative research, developing novel algorithms and computational pipelines, preparing research outputs for publication in high-impact journals, and supporting grant-related activities. The postholder will also engage in academic dissemination through conferences and workshops and contribute to the broader research objectives of the institution.

This is a fixed-term research position offering an opportunity to work within a highly collaborative and internationally recognised research setting, with access to state-of-the-art facilities and resources.

Eligibility Criteria:

• A PhD (or near completion) in Computer Science, Artificial Intelligence, Biomedical Engineering, or a closely related discipline
• Demonstrated experience in machine learning, deep learning, or computer vision
• Experience working with medical imaging or digital pathology data
• Strong programming background and experience handling large datasets
• Ability to work independently and as part of a multidisciplinary research team
• Evidence of research output or contributions to academic publications

Required Expertise/Skills:

• Proficiency in programming languages such as Python
• Experience with machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch)
• Strong understanding of image analysis and computer vision techniques
• Data preprocessing, model development, and evaluation skills
• Analytical thinking and problem-solving ability
• Scientific writing and communication skills
• Familiarity with research ethics and data governance

Salary Details:
£36,572 – £43,722 per annum (Grade 4)