Job's 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. However, a major challenge to train those DL methods is the availability of data. Here, simulations come in handy and could be used to support data accessibility. Unfortunately, the available simulations lack properties of real hardware (like non-idealities, non-symmetric behaviour), and thus, cannot be used efficiently.

Your task:

You will extend recent simulation tools to incorporate real-world properties. You either improve a custom python-based simulation, or create a Comsol simulation to mimic the physics of a real-world magnetic field.
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)
• OR Experience with multi-physic simulation tools like Comsol
• Optional: Basics in NMR
• Languages: English or German

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