PhD Candidate in AI-assisted medical imaging with MRI
Develop innovative Implicit Neural Representation models to improve MRI image reconstruction and diagnostic accuracy.
Best for:Physics, mathematics, or computer science graduates with a strong interest in medical imaging and machine learning.
Overview
The Department of Physics at NTNU is seeking a PhD candidate to join the MRI Physics Group for an interdisciplinary project focused on AI-assisted medical imaging. The research aims to utilize Implicit Neural Representation (INR) to reconstruct MR images directly from signals, specifically addressing magnetic field inhomogeneities in high-field 7T MRI systems. The successful candidate will work on developing physical models for neural network training, with initial applications in spinal cord functional MRI and potential extensions into industrial geology. This position offers a collaborative environment, access to state-of-the-art imaging facilities, and the opportunity to contribute to critical diagnostic research for neurodegenerative diseases.
What you'll do
- Simulate MRI data and model physical disturbances from various sources.
- Develop, implement, and train Implicit Neural Representation (INR) models for MRI time-series data.
- Analyze functional MRI data of the spinal cord.
- Disseminate research findings through academic publications.
- Present research at international conferences and professional meetings.
- Assist with departmental teaching activities.
Requirements
Degree required: Master's degree in physics, mathematics, biomedical engineering, computer science, or equivalent.
Degree offered: PhD
Language: English (fluent); Norwegian/Scandinavian is an advantage.
Instruction: English
- Strong academic background with a grade average of B or better.
- Proficiency in programming.
- Excellent oral and written communication skills in English.
- Must meet admission requirements for the PhD programme in Physics, Biophysics, or Medical Technology at NTNU.
- Master's degree must be completed and documented no later than 31 August 2027.
Funding & terms
How to apply
Applications must be submitted electronically via the Jobbnorge portal. Ensure all required documentation, including transcripts, thesis drafts, and a motivation letter, is attached before the deadline.
- CV
- Bachelor's and Master's transcripts
- Master's thesis or project description
- Motivation letter
- Publications or research work
- Certificates
- Contact information for two referees
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