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What you are going to do
We are looking for a PhD candidate who will substantially improve quantitative magnetic resonance imaging (MRI) image quality with model-based reconstruction and artificial intelligence.
Quantitative MRI allows doctors to quantitatively assess and characterise the state of a tumour and its microenvironment. This information can be used to personalise 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 personalising a treatment based on such biomarkers can substantially improve the effectivity of the treatment, we have multiple research lines that utilise quantitative MRI at the Amsterdam UMC.
However, current quantitative MRI approaches are slow and current reconstruction models are oversimplified, which results in poor image quality. Consequently, quantitative MRI is challenging to understand and interpreting such images is still only done at expert centres (such as the Amsterdam UMC). Hence, quantitative MRI is not being used routinely. 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 at each medical centre, greatly improving treatment outcomes worldwide.
Therefore, in this PhD-trajectory, the candidate will develop deep learning techniques to improve the state-of-the-art MRI reconstruction approach called model-based reconstruction. Our group has expertise in both machine learning for quantitative MRI and model-based reconstruction of quantitative MRI, which will allow for the perfect environment to bring this project to a successful end. Furthermore, we work closely together with doctors and clinical PhD candidates, enabling a rapid clinical evaluation and implementation of the techniques.
Your tasks will be:
What we expect from you
We are searching for candidates who want to apply their technical expertise to improve healthcare. On the one hand, the student must enjoy going into great technical detail, developing machine learning approaches that are informed by MRI physics. On the other hand, the candidate must function in a multidisciplinary team and collaborate with doctors to show the clinical advantages of the technique and get it implemented in the clinical workflow.
A candidate must have:
Additional skills we would like to see:
Fixed-term contract: 4 years.
What we offer you
We offer you ample opportunity for development, deepening and broadening, additional training and a place to grow! Working at Amsterdam UMC means working in an inspiring and professional environment where development is encouraged in every respect.
As PhD candidate, you have access to the Graduate School to further develop your skills. We offer a salary in line with the Collective Labor Agreement for University Medical Centers in the Netherlands (CAO UMC).
For an overview of all our other terms of employment, see https://werkenbijamc.nl/arbeidsvoorwaarden/.
Amsterdam UMC has an open culture. Together we continuously build an environment where everyone feels welcome. To achieve that, we strive to provide equal opportunities for everyone. We therefore cordially invite all interested parties to respond to this vacancy.
You will be part of the MRI-physics group at the Amsterdam UMC (location AMC). Dr. Oliver Gurney-Champion will be the 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 the 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.
Want to know more about working at AMR? Take a look at our website https://werkenbijamc.nl/werken-bij-amr/.
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