Job's description

Motivation: Nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) have become indispensable in various research fields such as physics, chemistry and medicine. They provide a non-invasive and non-destructive method for characterizing or imaging of a wide range of samples.
Recently, Raser-MRI (a new contrast mechanism without radio frequency pulses) has been developed, allowing for inexpensive hardware, higher resolution, and imaging without external rf-excitation. However, the contrast is based on cooperative nonlinear interaction between all slices of the image.
To efficiently model and correct for image artefacts, deep learning methods could be utilized to process MRI images.
Your task: You will use deep learning techniques to map simulated and acquired free induction decay (FID) signals to (1D+2D) MRI images. Your task includes finding the most suitable architecture structure (e.g. convolutional vs fully-connected vs recurrent neural network) to correlate FIDs to 1D MRI profiles, and to implement a functioning pipeline in pytorch.
You will be part of Prof. Korvink’s research group where you can get support from members with expertise in NMR theory, methodology, hardware, and simulation.

Personal qualifications:

• Highly motivated student with excellent academic record
• Excellent knowledge of programming language python (or similar)
• Experience with deep learning algorithms and knowledge about state-of-the-art methods
• Optional: Basics in NMR
• Languages: English or German

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