Marie Curie Doctoral Network Research Assistant

Marie Curie Doctoral Network Research Assistant - Strand, London, WC2R 2LS 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. To be considered for an interview, please make sure your application is full in line with the job specs as found below. 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. xpwpeyx 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.