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We are looking for a PhD candidate who, together with our team, will substantially improve quantitative magnetic resonance imaging (MRI) image quality using deep learning approaches.
Quantitative MRI allows healthcare providers to quantitatively assess and characterize the state of a tumour and its microenvironment. This information can be used to personalize cancer treatments. For example, a well-perfused tumour (quantified with MRI) will be more likely to react to chemotherapy than a tumour that is not perfused, as the chemo will need to reach the tumour. As personalizing a treatment based on such biomarkers can substantially improve the efficacy of the treatment, we have multiple research lines that utilize quantitative MRI at Amsterdam UMC.
However, current quantitative MRI approaches have notoriously poor image quality, low resolution and poor precision. Consequently, quantitative MRI is not used routinely in cancer care. If we can improve the image quality of quantitative MRI, it can be used in clinical routine to select the optimal treatment for each patient, greatly improving treatment outcomes worldwide.
Therefore, in this PhD-trajectory, the candidate will fundamentally change how qMRI is obtained and develop innovative explainable unsupervised physics-informed AI approaches for generating qMRI images that are of clinical quality. This will open the way to personalised treatments. These AI-driven frameworks will be tailored to suit the specific needs of qMRI. The candidate will further develop explainable AI and produce uncertainty estimates alongside the parameter maps. Finally, the candidate will test how accurately responding versus non-responding head-and-neck cancer patients receiving radiotherapy can be distinguished.
The position is financed by the NWO (TTW VIDI 2022).
Your main task will be to implement, optimize and test new approaches to AI-driven quantitative MRI.
We are searching for candidates who want to apply their technical expertise to improve healthcare. We expect the candidate to be able to learn and understand the technical details of MRI acquisition and reconstruction and advanced deep learning.
The candidate must function in a multidisciplinary team and collaborate with health care providers and MR-physics researchers.
A candidate must have:
Additional skills we would like to see:
You will be part of the MRI-physics group at Amsterdam UMC (location AMC). Dr. Oliver Gurney-Champion will be the co-promotor and daily supervisor and Prof. Aart Nederveen will be the promotor. The MRI-physics research group offers a resourceful and stimulating scientific environment. We are an energetic and enthusiastic team of roughly 15 BSc/MSc students, 20 PhD candidates, 3 postdocs, 5 assistant professors and two professors. The MRI-physics group is part of the Department of Radiology and Nuclear Medicine.
At Amsterdam UMC, we are equipped with 10 state-of-the-art 1.5/3 Tesla MRI scanners and a 7 tesla MRI scanner. We have reserved MRI research hours on most scanners.
Working at Amsterdam UMC means working in an inspiring and professional environment in which developing one`s talents and skills are encouraged. As a PhD candidate, you have access to the Graduate School to further develop your skills.
During the publication period, applications will be handled continuously. If the vacancy is filled, it will be closed prematurely.
If you have any questions about this position or want an informal chat about the project, please feel free to contact Dr. Oliver J. Gurney-Champion, Assistant Professor, via o.j.gurney-champion@amsterdamumc.nl or Natalia Korobova via n.korobova@amsterdamumc.nl.
For more information about the application procedure, please contact Name, Recruitment Advisor, via e-mail or via phone number.
A reference check, screening and hiring test may be part of the procedure. Read here whether that applies to you. If you join us, we ask you for a VOG (Certificate of Good Conduct).
Internal candidates will be given priority over external candidates in case of equal suitability.
Acquisition in response to this vacancy is not appreciated.