Marie Curie Doctoral Network Research Assistant

About Us

The School of Biomedical Engineering & Imaging Sciences at King's College London is a world-leading center for research and innovation in medical imaging, image analysis, and biomedical engineering. Our interdisciplinary research brings together engineers, computer scientists, physicists, and clinicians to develop cutting-edge technologies that transform healthcare.

This position is part of a prestigious Marie Skłodowska-Curie Actions (MSCA) Doctoral Network, offering exceptional training and networking opportunities across leading European research institutions. The MSCA programme supports the career development of early-stage researchers through international collaboration, specialized training, and exposure to both academic and industrial partners.

About The Role

We are seeking an exceptional early-stage researcher to join our MSCA Doctoral Network as DC5 (Doctoral Candidate 5), focusing on quantitative phenotyping via generative modelling of quantitative MRI data. This exciting PhD position combines advanced machine learning with medical imaging physics to develop next-generation tools for biomarker extraction and clinical decision support.

You will develop innovative generative models using vector quantised variational autoencoders (VQ-VAE) and transformer architectures to synthesise realistic quantitative MRI (qMRI) data with controlled anatomic variability. Your work will address a critical challenge in medical imaging: the limited availability of qMRI datasets. By augmenting models with phenotypes from non-quantitative MRI and disentangling image content from acquisition physics, you will create powerful tools that work across different imaging protocols.

A key aspect of your research will be developing physics-aware models that bidirectionally map between non-quantitative and quantitative MRI data. Using Bloch equation simulations and cycle-consistent learning, you will create physics-invariant networks for biomarker extraction, segmentation, and classification. This work has significant potential to improve the robustness and generalizability of AI tools in clinical settings.

The position offers unique international experience through a series of planned secondments at leading institutions, such as Amsterdam UMC (Netherlands) to apply your generative models to parameter mapping networks, University of Antwerp (Belgium) to validate your MRI simulator with preclinical data, and Institut Curie (France) to develop physics-invariant segmentation tools, to be discussed with the applicant

You will receive comprehensive training in Advanced Machine Learning for medical applications, Scientific Computing, Image Processing, and Image Acquisition, while working alongside world-class researchers in medical imaging and AI. Expected milestones include: (1) a covariate-conditioned generative model of qMRI data (M18), (2) a fast physics-based MRI simulator (M24), (3) a bidirectional non-quantitative to qMRI mapping model (M30), and (4) an integrated joint generative framework (M42).

This position is funded through a Marie Skłodowska-Curie Actions (MSCA) Doctoral Network grant and includes full enrollment in a doctoral programme with structured supervision and research training. The attractive salary package follows MSCA regulations, including living allowance, mobility allowance, and family allowance where applicable.

This is a full-time post (35 hours/week), and you will be offered a fixed-term contract for 3 years / until the end of the PhD.

About You

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

• Hold a Master's degree or equivalent in computer science, biomedical engineering, medical imaging, mathematics, or related discipline
• Strong background in machine learning and deep learning, particularly in generative models (VAEs, GANs, transformers)
• Programming experience in Python and deep learning frameworks (PyTorch)
• Knowledge of medical image processing and analysis techniques
• Ability to understand and implement mathematical models, including physics-based models
• Excellent written and oral communication skills in English
• Ability to work independently and collaboratively within an international research network
• Meet MSCA mobility rule: at time of recruitment, must not have resided or carried out main activity in the UK for more than 12 months in the 3 years immediately prior to appointment

Desirable criteria

• Experience with quantitative MRI (qMRI) techniques and physics
• Experience with vector quantised variational autoencoders (VQ-VAE) or similar models
• Knowledge of MRI physics and Bloch equation simulations
• Experience with medical image segmentation or classification tasks
• Publications in machine learning, medical imaging, or related fields

Downloading a copy of our Job Description

Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the page. This document will provide information of what criteria will be assessed at each stage of the recruitment process.

Further Information

We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community.

We are committed to working with our staff and unions on these and other issues, to continue to support our people and to develop a diverse and inclusive culture at King's.

As part of this commitment to equality, diversity and inclusion and through this appointment process, it is our aim to develop candidate pools that include applicants from all backgrounds and communities.

We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the person specification section of the job description. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.

To find out how our managers will review your application, please take a look at our ' How we Recrui t' pages.