Research Associate in Computational Pathology and Multimodal AI

On-site in Sheffield·Full-time·Added today (last checked 9 Nov)

University of Sheffield

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Our summary

Salary: £39,560 - £48,337 a year
About £2,670 - £3,190 a month after tax.
Work mode: On-site, Sheffield
Contract: Full-time
Holiday: 41 days holiday inc. bank holidays

Skills

The role

We are looking for a Research Associate to join the NEOPATH research group in the School of Clinical Dentistry, University of Sheffield. You will develop and validate deep learning methods that combine routine histology with radiology, multiplex imaging, spatial transcriptomics and clinical data to predict how oral and head and neck cancers behave and how they respond to treatment.

The group is led by Professor Ali Khurram, a Professor and consultant in oral and head and neck pathology. The group's work includes multicentre validated tools for detecting and grading oral epithelial dysplasia, and profiling tumour infiltrating lymphocytes in head and neck squamous cell carcinoma. Current programmes, funded by Cancer Research UK, Stand Up To Cancer and MRC, address cancer risk in oral potentially malignant disorders, and response to treatment.

Your research will centre on building and training models on large whole slide image, radiological and genomic cohorts for a range of cancer, linking image features to clinical outcomes and testing whether those models hold up on data from other centres and scanners. Alongside this, you will coordinate team meetings, work with our academic, clinical and patient collaborators, help develop grant applications and papers, and present our work at scientific and public events.

The post would suit someone with a PhD in a computational or quantitative field, or someone from a pathology or biomedical background who has built substantial programming and machine learning skills. A background in histopathology, radiology, omics and bio-informatics is a clear advantage, because much of the work sits where pathology and computation meet. We will provide clinical training for strong candidates from a computational background.

Main duties and responsibilities

Design, train and validate deep learning models for diagnosis, grading and outcome prediction in cancer.
Develop multimodal models that integrate histology with radiology, multiplex immunofluorescence or immunohistochemistry, spatial transcriptomics and clinical variables.
Build and maintain reproducible pipelines for slide preprocessing, stain normalisation, tissue and cell segmentation, feature extraction and model evaluation, using version control and clear documentation.
Curate and manage large imaging and clinical datasets in line with ethics approvals, data protection law and NHS information governance requirements.
Carry out external validation across centres and scanners, and assess model calibration, bias and failure modes.
Bring histopathological and computational thinking together, working with pathologists to design annotation protocols, check that model outputs make morphological sense and interpret explainability analyses.
Support the principal investigator in the day to day coordination of the group, including organising team meetings, tracking milestones and keeping work aligned across projects.
Act as a regular point of contact for academic, clinical and patient collaborators, and help sustain these working relationships.
Contribute to the supervision and support of postgraduate students working on related projects.
Contribute to grant and fellowship applications, including preliminary data, methods and supporting documents, and help identify suitable funding opportunities.
Lead and contribute to manuscripts, technical reports, and code and data releases.
Present the group's work at national and international conferences, seminars, and patient and public engagement events.
Keep up to date with the literature in computational pathology and medical AI, and bring relevant new methods into the group.
Follow University policies on health and safety, data protection, research integrity and sustainability.
Carry out other duties as required, commensurate with the scope and grade of the post.

Person Specification

Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and is respected. Even if your experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply.

Criteria	Essential or desirable	Stage(s) assessed at
PhD (or equivalent experience) in computational pathology, computer science, biomedical engineering, physics, mathematics or a related field, or a PhD in pathology or biomedical science with a substantial computational component	Essential	Application
Undergraduate degree in a relevant quantitative, computational, biomedical or clinical discipline	Essential	Application
Strong programming skills in Python and hands on experience with a deep learning framework such as PyTorch	Essential	Application/Interview
Experience of developing and evaluating machine learning or deep learning models for image analysis	Essential	Application/Interview
Sound understanding of model validation, including appropriate data partitioning, external testing and choice of performance metrics	Essential	Interview
Research outputs appropriate to career stage (for example publications, preprints, open source code or conference presentations)	Essential	Application
Excellent written and verbal communication, including the ability to explain technical work to clinical, academic and lay audiences	Essential	Application/Interview
Strong organisational skills, with the ability to coordinate work across a team and manage competing deadlines	Essential	Application/Interview
Ability to build effective working relationships with collaborators from different disciplines, and to work independently	Essential	Application/Interview
Training or experience in histopathology or a related clinical discipline	Desirable	Application/Interview
Experience with whole slide images and computational pathology tools (for example OpenSlide, QuPath, TIAToolbox)	Desirable	Application/Interview
Experience of integrating imaging with other data types, such as genomic, spatial transcriptomic, radiological or clinical data	Desirable	Application/Interview
Experience with pathology foundation models, transformers, self supervised learning or multiple instance learning	Desirable	Application/Interview
Experience of contributing to grant or fellowship applications	Desirable	Application
Experience of presenting research to patient, public or other non-specialist audiences	Desirable	Application/Interview
Experience of high performance computing, GPU clusters or containerised workflows (for example Slurm or Docker)	Desirable	Application
Experience of working with clinical or NHS data and the associated governance	Desirable	Application

Further Information

Grade	7
Salary	£39,560-£48,337
Work arrangement	Full-time, on campus
Duration	33 months (1 January 2027 to 30 September 2029)
Line manager	Academic Line Manager
Our website	https://www.sheffield.ac.uk/dentalschool
For informal enquiries about this job contact Professor Ali Khurram at s.a.khurram@sheffield.ac.uk

Next steps in the recruitment process

It is anticipated that the selection process will take place within 2 weeks of the application submission deadline. This will consist of an interview and a presentation. We plan to let candidates know if they have progressed to the selection stage within 1 week of the close of the application process. Contact j.l.young@sheffield.ac.uk if you require any reasonable adjustments.

Our vision and strategic plan

We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).

What we offer

A minimum of 41 days annual leave including bank holiday and closure days (pro rata) with the ability to purchase more.
Flexible working opportunities, including hybrid working for some roles.
Generous pension scheme.
A wide range of discounts and rewards on shopping, eating out and travel.
A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women's and Parent's networks).
Recognition Awards to reward staff who go above and beyond in their role.
A range of generous family-friendly policies
- paid time off for parenting and caring emergencies
- support for those going through the menopause
- paid time off and support for fertility treatment
- and more

More details can be found on our benefits page: sheffield.ac.uk/jobs/benefits (opens in a new window).

We are a Disability Confident Employer. If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.

We are a research university with a global reputation for excellence. Our ideas and expertise change the world for the better, making a real difference to society. We know that when people come together with different views, approaches and insights it can lead to richer, more creative and innovative teaching and research and the highest levels of student experience. Our University Vision (www.sheffield.ac.uk/vision) outlines our commitment to building a diverse community of staff and students that recognises and values the abilities, backgrounds, beliefs and ways of living for everyone.

The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.

We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more. Find out more about our benefits (opens in a new window) and join us to become part of something special.