Job Description

Ref Number

B04-06175

Professional Expertise

Research and Research Support

Department

UCL BEAMS (B04)

Location

London

Working Pattern

Full time

Salary

See advert text

Contract Type

Fixed-term

Working Type

Hybrid

Available for Secondment

No

Closing Date

26-Jun-2025

About us

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This role forms part of the PROVIDENTIA 1000 - Proton and radiation data combined with biology, imaging and long term outcomes to advance radiation combined modality treatments in children and young people project. The project is assembling a comprehensive database of 1000 children and young people treated with radiation (proton and photon) focussing on brain malignancies to answer three questions: Can we refine radiation indications to improve outcomes using unbiased molecular and imaging data? Can we propose new solutions to mitigate toxicity development? Can we use AI to generate high-fidelity virtual synthetic data on a relatively limited real-world dataset?



Towards leveraging the PROVIDENTIA database to generate novel discoveries into the treatment responses of children and young peoples' brain tumours, the Neuro-oncology Imaging within High-Dimensional Neurology, led by Dr Harpreet Hyare, and Computational Radiation Biology Lab, led by Dr Jamie Dean, are collaborating to perform state of the art computational modelling of these multimodal data, encompassing extensive MRI, molecular and clinical measurements.



The High-Dimensional Neurology Group led by Prof Parashkev Nachev, develops complex, high-fidelity models of brain dysfunction deployable within established clinical pathways, creating end-to-end prototype systems rapidly translatable into clinical reality. It operates across neurology, with an emphasis on actionable disorders with substantial population-level footprint, such as stroke and brain tumours.



The Group is based in the Department of Translational Neuroscience and Stroke, UCL Queen Square Institute of Neurology, which aims to understand mechanisms underlying neurological diseases and develop new treatments for patients with neurological conditions. The Department brings together several different areas of basic and clinical neuroscience, encouraging collaboration both within its various teams as well as with other departments across the Institute of Neurology, the National Hospital for Neurology & Neurosurgery, the Faculty of Brain Sciences and UCL at large.



The Computational Radiation Biology Lab seeks to unlock the "fourth dimension" of radiation biology, time, to design superior radiotherapy strategies. Whilst radiotherapy is an effective treatment for numerous cancer patients, for many others it fails to control their tumours. Increasing the rate of treatment success requires understanding and manipulating the temporal evolution of tumours and normal tissues in response to therapy. Through an innovative, multidisciplinary, collaborative approach, integrating mathematical modelling of molecular and cellular radiation response dynamics with longitudinal measurements of the response of cells, mouse models and patients to treatment, the lab is gaining a detailed, quantitative understanding of the dynamic tumour and normal tissue responses to radiotherapy and drug-radiation combination therapies. The lab is then leveraging this knowledge of the "fourth dimension" of radiation biology to design and evaluate superior treatment strategies.

About the role

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We are seeking a Research Fellow with a background in medical image computing or machine learning to join the High-Dimensional Neurology Group and Computational Radiation Biology Lab at UCL. The successful candidate will perform computational modelling of multimodal MRI, molecular and clinical data of children and young peoples' brain tumours, collected as part of the PROVIDENTIA project, under the supervision of Dr Harpreet Hyare and Dr Jamie Dean.



The initial focus will be on machine learning-based analyses of the MRI data to understand patterns of treatment response and recurrence, followed by integration of these data with clinical and molecular data to infer clinical and molecular features associated with differential responses between patients.



The post is funded by Cancer Research UK for approximately 11 months in the first instance.



If you have any queries about the role, please contact h.hyare@ucl.ac.uk and jamie.dean@ucl.ac.uk.

About you

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Research Fellow in Computational Neuro-Oncology



The successful applicant will have a Bachelor's degree (or equivalent) in Engineering, the Physical Sciences or Computer Sciences, and have completed or be near the completion of a PhD in medical imaging, medical physics, biomedical engineering, computer science, physics, engineering, statistics, mathematics, neuroscience, or similar.



The applicant will have experience in computer programming and machine learning. Excellent collaborative interpersonal skills, with an ability to work co-operatively in a multidisciplinary setting, and writing and presentation skills are also necessary.



The role will require the applicant to develop, refine and evaluate segmentation models for children and young peoples' brain tumours, and develop prototype systems to improve survival prediction, integrate multimodal imaging, molecular and clinical data and assist clinical decision making. The applicant will prepare and analyse data for research publications and dissemination of findings at conferences, internal meetings within UCL, and with collaborators. The role will involve working collaboratively with a multidisciplinary team of medical doctors and academics with computational modelling backgrounds. Your application should include a CV and a Cover Letter: In the Cover Letter please evidence how you meet the essential and desirable criteria in the Person Specification part of the . 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 it will allow.)



Research Fellow: Appointment at Grade 7, spine point 31 (44,480), is contingent upon the candidate having been awarded a PhD. If the PhD has not yet been awarded, the initial appointment will be made at Grade 6B (41,255 per annum), with the title Research Assistant. Upon final submission of the PhD thesis, the position will be regraded to Grade 7 Research Fellow, and the salary will be backdated accordingly.

What we offer

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As well as the exciting opportunities this role presents we also offer some great benefits some of which are below:


41 Days holiday (27 days annual leave 8 bank holiday and 6 closure days) Additional 5 days' annual leave purchase scheme Defined benefit career average revalued earnings pension scheme (CARE) Cycle to work scheme and season ticket loan Immigration loan Relocation scheme for certain posts On-Site nursery On-site gym Enhanced maternity, paternity and adoption pay Employee assistance programme: Staff Support Service Discounted medical insurance

Visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits to find out more.



The advert will close 26 June 2025 at 23:59 GMT, however we may close applications early if we receive a high volume of applications. Early application submission is recommended.



A job description and person specification can be accessed at the bottom of this page.

Our commitment to Equality, Diversity and Inclusion

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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; 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/