Job Purpose

This is an exciting research opportunity to join the laboratories of Professor David Chang and Dr Ke Yuan (School of Cancer Science) and use a multidisciplinary approach to study artificial intelligence and machine learning applications in the management of pancreatic cancer. Pancreatic cancer is soon to be the second highest cancer related mortality in the western societies, and its overall survival has not changed significantly in the last few decades.  While significant understanding in the molecular pathology of pancreatic cancer has been made through large scale genomic and transcriptomic sequencing projects, the full utility of molecular data, especially in a multi-omics analysis fashion has not been fully explored and capitalised with the utilisation of artificial intelligence and machine learning.  The post holder will perform research on machine learning and AI approaches to better prognosticate and better prediction of treatment response in pancreatic cancer utilising genomics, transcriptomic, digital pathology (H&E, immunohistochemistry and multiplex immunofluorescence).

This post is funded through a research grant from Astra Zeneca to study the interaction between pancreatic cancer epithelial compartment and the compositions of tumour microenvironment using multi-omics approach, including immunohistochemistry, multiplex immunofluorescence, genomics and transcriptomics. The post is ideally suited for a motivated individual with a PhD in a relevant field, be driven by scientific curiosity, and have an excellent publication record for their career stage and scientific background. Ideally the successful candidate would have a strong track record or keen interest in artificial intelligence and machine learning, and a deep understanding of the associated experimental methodologies. Experience with genomics and digital pathology analysis would be an advantage.

About You

The ideal candidate will have a PhD in computational sciences (deep learning, computer vision, medical image analysis, bioinformatics, biostatistics, genomics) and experience doing research in the field of digital pathology or cancer genomics, although we welcome applications from candidates with diverse educational backgrounds, including computational biology, physics and (bio)statistics, biology, or medicine. Applicants should have a proven publication record.

A computing background is not strictly necessary, but you must be keen to work in a dry setting. For applicants with computing experience, fluency in Linux/Unix, and excellent knowledge in a programming language is expected such as Python, R/BioConductor. Prior experience with digital pathology or genomics analysis are considered an advantage.

Main Duties and Responsibilities

1.        Take a leading role in the planning and conduct of assigned research individually or jointly in accordance with the project deliverables and project/group/School/College research strategy.

2.        Document research output including analysis and interpretation of all data, maintaining records and databases, drafting technical/progress reports and papers as appropriate.

3.        Establish and maintain your research profile and reputation and that of The University of Glasgow/ School/ Research Group, including establishing and sustaining a track record of independent and joint publications of international quality in high profile/quality refereed publications, enhancing the research impact in terms of economic/societal benefit, and gathering indicators of esteem.

4.        Survey the research literature and environment, understand the research challenges associated with the project & subject area, & develop/implement a suitable research strategy.

5.        Presentation of work at international and national conferences, at internal and external seminars, colloquia and workshops to develop and enhance our research profile.

6.        Take a leading role in the identification of potential funding sources and to assist in the development of proposals to secure funding from internal and external bodies to support future research.

7.        Take a leading role in developing and maintaining collaborations with colleagues across the research group/School/College/University and wider community (e.g. Academic and Industrial Partners).

8.        Take a leading role in team/group meetings/seminars/workshops and School research group activities to enhance the wider knowledge, outputs and culture of the School/College.

9.        Take the lead in the organisation, supervision, mentoring and training of undergraduate and/or postgraduate students and less experienced members of the project team to ensure their effective development.

10.     Perform administrative tasks related to the activities of the research group and School, including Budgets/Expenditure.

11.     Make a leading contribution to Teaching activities (e.g. demonstrating etc) and associated admin as assigned by the Head of School and in consultation with Principal Investigator.

12.     Keep up to date with current knowledge and recent advances in the field/discipline.

13.     Engage in personal, professional and career development, to enhance both specialist and transferable skills in accordance with desired career trajectory.

14.     Undertake any other reasonable duties as required by the Head of School/Director of Research Institute.

15.     Contribute to the enhancement of the University’s international profile in line with the University Strategy.

Knowledge, Qualifications, Skills and Experience

Knowledge/Qualifications Essential:

A1 An awarded PhD with at least 2 years’ post-doctoral experience or equivalent in Computer Science / Statistics / Computational Biology or a related area (or having submitted a PhD thesis prior to taking up the appointment).

A2 Good understanding of the state-of-the-art in deep learning in computer vision or medical images or computational biology.

A3 Proficient of programming using Python, and deep learning frameworks such as TensorFlow, PyTorch.

Desirable:

B1 A comprehensive and up-to-date knowledge of current issues and future directions in the use of deep learning in digital pathology or multi-omics approaches.

Skills

Essential:

C1 Proven skills in a field relevant to at least one of the following: deep learning in computer vision, deep generative models, self-supervised models, genomics analysis, and medical image analysis such as histopathology.

C2 Research creativity and strong cross-discipline collaborative ability.

C3 Excellent communication skills (oral and written), including public presentations and ability to communicate complex data/concepts clearly and concisely

C4 Excellent interpersonal skills including team working and a collegiate approach

C5 Appropriate workload/time/project/budget/people management skills

C6 Extensive IT and data analysis/interpretation skills as appropriate 

C7 Self-motivation, initiative and independent thought/working

C8 Problem solving skills including a flexible and pragmatic approach

C9 Good Team Leadership skills including demonstrable supervisory skills

Experience

Essential:

E1 Sufficient relevant research experience

E2 Experience in a field relevant to at least one of the following: deep learning in computer vision, deep generative models, self-supervised models, genomics analysis, and medical image analysis including digital pathology.

E3 Experience of programming in a scientific setting

E4 Proven ability to deliver quality outputs in a timely and efficient manner

E5 A track record of presentation and publication of research results in quality journals/conferences

Informal enquiries should be directed to Professor David Chang, David.Chang@glasgow.ac.uk or Dr Ke Yuan Ke.Yuan@glasgow.ac.uk.

Terms and Conditions

Salary will be Grade 7, £40,247 - £45,163 per annum.

This post is full-time and has funding for up to 2 years.

Closing Date:  19 March 2025 at 23:45