Job description
Motivation: Nuclear magnetic resonance (NMR) has become indispensable in various fields of research such as physics, chemistry and medicine. It provides a non-invasive and non-destructive method for examining a wide range of samples, for example to find out the composition of SARS-CoV-2 at an atomic level.
A homogeneous magnetic field is essential for an accurate and precise result, but this is – among other factors – altered by the sample to be examined. The magnetic field can be adjusted by so-called "shim coils", but this procedure must, in many times, be carried out manually and requires a lot of time and experience. This process is called "shimming".
To make this work easier, various algorithms have been used that relieve humans of this task, including deep learning (DL) based methods.
Your task: You will extend and improve recent advances of DL-based NMR shimming (see https://doi.org/10.1016/j.jmr.2022.107151). First, by improving the theoretical limitations of current approaches through state-of-the-art techniques. And second, the method will be transferred and tested on real-world experiments.
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.
Starting date: As soon as possible
Contract duration: 6 months
Qualification:
- 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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