Enhancing the resolution of NMR spectra of liquids and solids using machine learning trained on[...]
Offer Description
Background and motivation : In recent years, the advent of NMR magnets producing magnetic fields higher than 23 T, in which Larmor frequency, ν0(1H), exceeds 1 GHz, has enabled the acquisition of NMR spectra with unparalleled resolution, allowing to separate similar chemical environments in molecules or materials. This gain in resolution is particularly useful for determining the atomic‑scale structure of biological macromolecules, such as proteins, or inorganic and hybrid materials containing quadrupolar nuclei, such as 27Al or 17O. However, the use of ultra‑high field NMR spectroscopy is limited by the cost of NMR magnets producing magnetic fields higher than 23 T. Owing to this high cost, the number of ultra‑high field NMR magnets is much scarcer than that of lower field magnets.
Approach : This project aims at exploring how the resolution gains obtained with ultra‑high field NMR spectrometers can be transferred to spectra recorded at lower fields through the use of machine learning‑based tools. This approach requires training machine/deep learning tools on simulated and experimental NMR datasets obtained at different magnetic fields ranging from 9.4 to 28.2 T (i.e., 400 MHz ≤ ν0(1H) ≤ 1.2 GHz). It will be tested on different types of samples, such as proteins or small molecules in solution, as well as inorganic and hybrid materials containing quadrupolar nuclei. This project will benefit from the presence in Lille of NMR spectrometers with different fields, including a 1.2 GHz NMR spectrometer that is unique in France.
The candidate : We seek application from national and international students who have graduated in chemistry, physics, materials science or data science, preferably with a background in NMR spectroscopy. We are committed to equal opportunity recruitments. IMMENSE project is dedicated to promoting the role of women in science, and, therefore, explicitly invites women to apply.
Applications (cover letter, CV, transcripts of grades and names for recommendation) and informal queries about the lab and research projects should be directed by email to
Where to apply
Requirements
- Research Field Chemistry Education Level Master Degree or equivalent
- Research Field Physics Education Level Master Degree or equivalent
- Research Field Computer science Education Level Master Degree or equivalent
- Research Field Information science Education Level Master Degree or equivalent
- Research Field Engineering Education Level Master Degree or equivalent