Research Associate* in Trustworthy MRI Reconstruction with Uncertainty Modelling

We are looking for an enthusiastic research associate (post‑doctoral) / research assistant (pre‑doctoral) to make a leading contribution to a project on TrustMRI: Trustworthy and Robust Magnetic Resonance Image Reconstruction with Uncertainty Modelling and Deep Learning. The objective of the post is to enable the advancement towards trustworthy and robust AI‑based MRI reconstruction, through equipping them with the ability to model uncertainty and handle cases outside distribution. MRI is the leading diagnostic modality for a wide range of exams, but the physics of its data acquisition process makes it inherently slow. Recently, AI techniques have opened the possibility to accelerate this considerably, however, the lack of consideration of their trustworthiness and failure management limits their translational potential in clinical practice. The project will aim to develop advanced probabilistic deep learning methods that can reliably quantify and evaluate uncertainty for AI‑based MRI reconstruction, as well as leveraging that for robust and adaptive deployment. The project will involve interdisciplinary research and close collaborations with academic and industrial partners. Candidates with expertise in deep generative models, probabilistic modelling, active learning and computer vision/medical imaging are preferred.

The successful candidate will be hosted by Department of Electrical and Electronic Engineering and the College’s new I‑X initiative, under the joint supervision of Dr Chen Qin and Dr Yingzhen Li, and work with a team of researchers and PhD students at Biomedical Image Analysis Group.

Key responsibilities include:

• To take initiatives in the planning of research
• To conduct original research with appropriate supervision
• To write scientific papers and submit publications to high‑quality conferences and journals
• To present the research at leading international conferences
• To open‑source experimental software prototypes
• To assist in the supervision of undergraduate and postgraduate research students as required

What we are looking for:

To apply for this position, you must have a strong background in a subject relevant to computer science, mathematics, or a closely related discipline, and have experience, including a proven publication track record, in one or more of the following areas: Probabilistic/Bayesian deep learning, uncertainty modelling, machine learning, computer vision and/or medical imaging. You should also have:

• Excellent communication and academic writing skills, demonstrated through a publication track record and/or presentations at scientific events
• The ability to organise and prioritise your own work with minimal supervision
• Strong analytical, problem‑solving, organisational, and interpersonal skills

At Research Associate level you must have been awarded a PhD (or equivalent) in a subject relevant to the post. At Research Assistant level you will need to have a good (1st or 2:1) master and undergraduate degree in a relevant discipline.

What we can offer you:

The opportunity to continue your career at a world‑leading institution and be part of our mission to continue science for humanity. Grow your career: gain access to Imperial’s sector‑leading dedicated career support for researchers as well as opportunities for promotion and progression. Sector‑leading salary and remuneration package (including 39 days off a year and generous pension schemes). Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.

Further information:

Candidates should attach the below documents in the online application:

• A full CV, with a list of all publications
• A 2‑page research statement and proposal indicating what you see are interesting research issues relating to the above post and why your expertise is relevant.
• Any element relating your experience / passion for software engineering (blog, open‑source projects, GitHub repositories, and others) will be carefully inspected.

Should you require any further details on the role please contact: Dr Chen Qin – c.qin15@imperial.ac.uk.

Please note that job descriptions cannot be exhaustive, and the post‑holder may be required to undertake other duties, which are broadly in line with the above key responsibilities.

Equal Opportunities:

We work towards equality of opportunity, eliminating discrimination, and creating an inclusive working environment for all. We encourage applications from all backgrounds.