Research Fellow

University College London Hospitals NHS Foundation Trust; City of Westminster; On-site; GBP 56,000 - 65,000; Full time

Benefits offered by this job

41 Days holiday (27 days annual leave

8 bank holidays

6 closure days

Additional annual leave purchase

CARE pension scheme

Cycle to work scheme

Season ticket loan

Immigration loan

On-site nursery

On-site gym

Enhanced maternity/paternity/adoption

Staff Support Service

Discounted medical insurance

Job summary

The University College London Hospitals NHS Foundation Trust seeks a Senior Research Fellow to advance an EPSRC project on a radical new brain-imaging modality using low-frequency ultrasound and full-waveform inversion.

You will develop machine-learning methods to speed and improve FWI on large clinical imaging data, building priors for image reconstruction and evaluating results for acute stroke questions.

Qualifications

• PhD or equivalent research experience in ML for imaging or inverse problems.
• Strong track record in developing deep generative models with large‑scale data.
• Experience with physics‑driven inverse problems, learned priors or physics‑informed methods.

Responsibilities

• Develop ML methods for fast, robust FWI in brain imaging.
• Work across machine learning, imaging and clinical research collaborations.
• Evaluate reconstructed images on clinically relevant questions in acute stroke.
• Communicate results with researchers, engineers, clinicians.

Skills

Python

Deep learning

GPU/HPC

Imaging ML

Education

PhD in machine learning, computer science, computational physics, applied mathematics or closely related discipline

Job description

We are seeking a Senior Research Fellow to work on an EPSRC-funded project developing a radically new brain‑imaging modality using low‑frequency ultrasound and full‑waveform inversion (FWI), with the aim of generating high‑resolution images of the living adult human brain. You will develop machine‑learning methods to make FWI faster and more robust, using large‑scale clinical imaging data to build generative models of the skull and brain and provide anatomically informed priors for image reconstruction. You will investigate the trade‑offs between reconstruction speed and accuracy and evaluate the resulting images against clinically important questions in acute stroke. Working across machine learning, imaging and clinical research, you will play a key role in developing technology with the potential to deliver safe, fast and portable brain imaging for stroke and other acute neurological conditions, including outside hospital settings. The role is available from 01 November 2026 and is funded by the Engineering and Physical Sciences Research Council (EPSRC) until 31 October 2027 in the first instance. If you need reasonable adjustments or a more accessible format to apply for this job online, or have any queries regarding the application process, please contact the Institute of Neurology HR Team ( ). Informal enquiries regarding the role can be addressed to Professor Parashkev Nachev (email: ). A full job description and person specification for this role can be accessed below.

You will have a PhD in machine learning, computer science, computational physics, applied mathematics or a closely related discipline, or equivalent relevant research experience, together with a strong track record of research in machine learning for imaging or inverse problems. You will have substantial experience developing and evaluating deep generative models using large‑scale scientific or imaging data, as well as experience with physics‑driven inverse problems, learned priors or physics‑informed methods. Strong Python and deep learning programming skills, including experience with GPU/HPC environments, are essential. You will be able to work independently and take ownership of research within a collaborative, multi‑disciplinary project. Excellent communication skills and the ability to work effectively with researchers from different disciplines, including engineers, physicists and clinicians, are also important. 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.

The High‑Dimensional Neurology Group, led by Professor Parashkev Nachev within the Department of Translational Neuroscience & Stroke, applies large‑scale machine learning to routinely acquired clinical data, particularly brain imaging, to build generative and predictive models of the brain in health and disease and translate them into tools that improve the care of patients with acute neurological disorders such as stroke. The group works closely with clinical services at the National Hospital for Neurology and Neurosurgery and with University College London Hospitals NHS Foundation Trust. It also has access to some of the largest curated clinical neuroimaging datasets in the UK.

Starting salary offered in the range £56,431 - £64,644 per annum, inclusive of London Allowance. As well as the exciting opportunities this role presents, we also offer 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
• On‑site nursery
• On‑site gym
• Enhanced maternity, paternity and adoption pay
• Employee assistance programme: Staff Support Service
• Discounted medical insurance, We will consider applications to work on a part‑time, flexible, and job share basis wherever possible.

This appointment is subject to UCL Terms and Conditions of Service for Research and Professional Services Staff. Please visit for more information.

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12% of Institute staff are actively working on Equality, Diversity and Inclusion (EDI) initiatives; visit our for more information about our initiatives and priorities.

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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 therefore particularly encourage applications from candidates who are likely to be under‑represented in UCL's workforce; these include people from Black, Asian and ethnic minority backgrounds, disabled people, LGBTQI+ and gender diverse people in all roles, and women in Grade 9 and 10 roles.