Foundation Models for Magnetic Resonance Spectroscopy

Tuesday 31st March 2026

Contact Email for the Job Positing renaud.lopes@univ-lille.fr
Organization University of Lille
Location Lille - France
Title Foundation Models for Magnetic Resonance Spectroscopy
URL https://scholar.google.com/citations?user=d8l23kEAAAAJ&hl=fr
Closing date Jun 21, 2026
Description Project
This PhD position is part of the IMMENSE interdisciplinary project, which aims to develop new magnetic resonance methods combining spectroscopy, imaging and artificial intelligence to improve diagnostics in medicine and characterization tools in chemistry and materials science.
Magnetic resonance spectroscopy (NMR/MRS) provides unique information about molecular composition in fields ranging from brain metabolism to structural biology and materials science. However, spectral resolution strongly depends on the magnetic field strength. High-field instruments (e.g., 7 T MRI or ultra-high-field NMR) provide much higher spectral resolution than widely available lower-field systems.
This PhD project aims to explore a new paradigm:
Can AI reconstruct high-field spectral information from low-field measurements?
The goal is to develop foundation models for spectroscopy, capable of learning general representations of NMR signals and enhancing spectral resolution across different instruments and applications.
Research Objectives
The PhD will develop AI models for spectral representation learning and super-resolution applied to magnetic resonance spectroscopy.
Main objectives include:
1. Foundation models for spectroscopy: Develop deep learning models capable of learning general representations of NMR spectra across different magnetic fields and experimental conditions.
2. Spectral super-resolution: Train models to reconstruct high-resolution spectra from low-field acquisitions (e.g., 3 T → 7 T brain MR spectroscopy).
3. Cross-domain generalization: Investigate whether learned representations generalize across: brain metabolite spectroscopy, protein NMR, small-molecule spectroscopy, solid-state NMR for materials.
4. Spectroscopy datasets and benchmarks: Contribute to building standardized datasets of paired low-field and high-field spectra for training and evaluation.
Candidate Profile
We are looking for a highly motivated candidate with a background in:
Required
• Artificial Intelligence / Machine Learning
• Computer Science or Signal Processing
• Python and deep learning frameworks (PyTorch preferred)
Preferred
• generative models or representation learning
• medical imaging or spectroscopy
Education
Master’s degree or engineering degree in AI, computer science, applied mathematics, signal processing, physics or biomedical engineering.
Research Environment
The PhD will be conducted within an interdisciplinary environment combining:
• expertise in AI, spectroscopy, neuroscience, and chemistry
• clinical 3 T and 7 T MRI scanners
• ultra-high-field NMR spectrometers
How to Apply
Applicants should send to Renaud LOPES (renaud.lopes@univ-lille.fr):
CV + motivation letter + academic transcripts + contact details of two referees