Call for applications for a fully financed PhD fellowship
Life creates order in a universe where disorder always increases. The set of chemical reactions enabling this —metabolism— changes as we eat, sleep, exercise, or become unwell. This PhD studentship is for a transformative research project integrating physics, mathematics, artificial intelligence (AI), and cutting-edge medical imaging to unravel and quantify these metabolic processes inside people.
Hyperpolarized MRI (HP-MRI) uses quantum mechanics to boost the sensitivity of MRI by orders of magnitude, allowing real-time imaging of metabolites like pyruvate as they are processed in vivo. Unlike conventional imaging techniques such as PET, HP-MRI avoids ionizing radiation, measures multiple metabolites simultaneously, and directly maps reactions like glycolysis and oxidative phosphorylation. It is uniquely powerful for diagnosing and monitoring diseases like cancer, multiple sclerosis, and Alzheimer’s, with its clinical potential currently being explored in multinational trials.
The EU-funded project "Quantum+AI for Diagnostics" (Q-AID) tackles one of the most fascinating challenges in medical imaging: mapping the relationship between underlying biological "truths" (e.g., tumor metabolism) and the signals captured by imaging systems, which are subject to noise, artefacts, and are imperfect. These mappings are deeply complex, involving layers of transformation —signal generation, reconstruction, and visualization— before even addressing the biological dynamics of living organisms. Metabolic systems are vast, interwoven reaction networks, where only a subset of processes can be measured directly. Using "physics-informed" neural networks, we aim to combine analytical knowledge with AI tools to infer unseen processes.
In this studentship, you will:
• Develop super-resolution models to overcome the spatial resolution limitations of HP-MRI, leveraging data from conventional imaging
• Model imaging processes mathematically, bridging biological truths and observable signals.
• Design AI approaches for semantic segmentation and metabolic classification, integrating multimodal imaging datasets.
• Quantitatively unify HP-MRI with other modalities like PET and CT.
• Work closely with clinicians to build life-saving tools that will impact patient care
The candidate will ideally be a physicist, biomedical engineer, mathematical biologist, computer scientist, or other person with a quantitative background eager to apply their skills in a very medically relevant field, eager to work in a multidisciplinary field, and to learn about areas of expertise beyond their own. We would ordinarily expect those applying to have a masters' degree in a relevant area, broadly interpreted.
Please submit your application via this link. Application deadline is 1 May 2025 at 23:59 CET. Preferred starting date is 1 September 2025 or as soon as possible hereafter.
For information about application requirements and mandatory attachments, please see our application guide.
Please contact Associate Professor Jack Miller, jack.miller@clin.au.dk, for more information.
All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.
