Research Fellow in Prostate Cancer Image Analysis London, England
Job description
About us
UCL has a well-established reputation as one of world’s top research universities and was ranked second in the UK in research power in the latest Research Excellence Framework assessment (REF 2021). The UCL Department of Medical Physics and Biomedical Engineering is one of the largest and oldest academic departments in the UK and equally has an outstanding reputation for research. Researchers in the Department contributed to the UCL Faculty of Engineering Science’s submissions in REF 2021, 97% of which were rated as either ‘world-leading' or ‘internationally excellent’. The Computer Assisted Navigation, Diagnosis and Intervention (CANDI) research group, led by Dr Yipeng Hu and Professor Dean Barratt, is a friendly and dynamic team comprising around 15 research staff and students who are committed to using medical imaging to improve disease diagnosis and the safety and accuracy of minimally-invasive surgical and non-surgical interventions. The group aims to develop innovative technologies, harnessing the latest advances in imaging, artificial intelligence and computational modelling, and enjoys close collaborations with leading clinical and non-clinical research teams at UCL and internationally. An exciting opportunity has arisen to join the CANDI group to work on machine-learning-based analysis of magnetic resonance images to better diagnose prostate cancer in collaboration with researchers from Stanford University and the OHSU Knight Cancer Centre in the USA.
About the role
This is a research-focused role that requires a PhD (or equivalent) and skills and experience in computational image analysis using machine learning techniques, evidenced by a track record of peer-reviewed academic publications. The main responsibilities of the role are to carry out and publish research into the application of contemporary machine learning techniques to analyse magnetic resonance and histological images of prostate cancer, working over two projects funded by Cancer Research UK, within a team of engineers, scientists, and clinicians.
This is a fixed-term role of 2 years in the first instance. Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, the initial appointment will be made at Grade 6B (salary: £34,819-37,548 per annum) with payment at Grade 7 being backdated to the date of the submission of the PhD thesis.
This role meets the eligibility requirements for a skilled worker certificate of sponsorship or a global talent visa under UK Visas and Immigration legislation. Therefore, UCL welcomes applications from international applicants who require a visa.
For more information about this role, please contact [email protected]
This role is eligible for hybrid working with a minimum of 40% of time spent working on-site.
About you
The successful candidate will have, or be very close to completing, a PhD in computational image analysis or closely related subject. They will have excellent computer programming skills, a thorough understanding of the theory and practice of developing neural network models for image analysis tasks, and a track record of publishing and presenting their research in peer-reviewed academic journals and international conferences. Excellent written and verbal communication skills and the ability to be innovative and pragmatic, developing novel solutions to real-world medical image analysis problems, are very important to this role, as is the ability to work both independently and collaboratively within a team. Some experience in analysing medical images (in particular, magnetic resonance and/or tissue histology images), and working closely with clinicians on medical or biomedical applications, is desirable, but not essential, and candidates with a strong background in machine-learning-based analysis of non-medical images are encouraged to apply.
Please refer to the Person Specification for a comprehensive list of the requirements for this role. Your application should include both a CV and a short cover letter that outlines how you meet the essential and desirable criteria in the Person Specification part of the Job Description.
Please upload this in the cover letter attachment section of the application form. By including a cover letter, you can leave blank the 'Why you have applied for this role?' field in the online application form, which is limited in the number of characters permitted.
A job description and person specification can be accessed at the bottom of this page.
If you need reasonable adjustments or a more accessible format to apply for this job online, or have any queries about the role or the application process, please contact Professor Dean Barratt ([email protected])
What we offer
As well as the exciting opportunities this role presents we also offer some great benefits some of which are below:
- 41 Days holiday (including 27 days annual leave 8 bank holiday and 6 closure days)
- Defined benefit career average revalued earnings pension scheme (CARE)
- Cycle to work scheme and season ticket loan
- On-Site nursery
- On-site gym
- Enhanced maternity, paternity and adoption pay
- Employee assistance programme: Staff Support Service
- Discounted medical insurance
For more information, visit https://www.ucl.ac.uk/human-resources/pay-benefits/staff-benefits
Our commitment to Equality, Diversity and Inclusion
As London’s Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world’s talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong.
We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL’s workforce.
These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women.
You can read more about our commitment to Equality, Diversity and Inclusion here : https://www.ucl.ac.uk/equality-diversity-inclusion/
