Applications are invited for a fully funded PhD position in the UltraHighResNMR project at the Physical Chemistry and Chemistry of Life (CPCV) research unit, École Normale Supérieure (ENS) – PSL University in Paris, France. This interdisciplinary doctoral project combines advanced Nuclear Magnetic Resonance (NMR) spectroscopy, deep learning, and physical chemistry to study ionic liquids for next-generation battery electrolytes.
Scholarship Overview
| Designation | PhD Fellow / Doctoral Researcher |
|---|---|
| Host Institution | École Normale Supérieure (ENS), PSL University |
| Research Unit | Physical Chemistry and Chemistry of Life (CPCV) UMR 8228 |
| Location | Paris, France (with a secondment in Rostock, Germany) |
| Research Field | NMR Spectroscopy, Artificial Intelligence, Physical Chemistry, Energy Materials |
| Application Deadline | October 31, 2026 |
| PhD Start Date | March 1, 2027 |
Project Description
The UltraHighResNMR project aims to significantly enhance NMR spectral resolution using deep learning convolutional neural networks. The developed methodologies will be applied to investigate molecular dynamics and interactions in complex ionic liquids—specifically solvate ionic liquids (SILs) and water-in-solvate ionic liquids (WISILs)—which represent promising, safer, and environmentally friendly alternatives for battery electrolytes.
The successful candidate will use a world-unique 900 MHz NMR spectrometer equipped with high-resolution relaxometry prototypes. Key project tasks include:
- Expanding the joint time-frequency WaveNet neural network architecture to predict pure-shift high-resolution relaxometry spectra.
- Investigating rotational, translational diffusion, and site-specific dynamics across relaxation fields from 100 µT to 21 T.
- Integrating experimental relaxometry data with advanced theoretical models and molecular dynamics simulations.
- Participating in an international collaboration, including a two-month research secondment at the University of Rostock in Germany.
Research Area
- Physical Chemistry & Biophysical Chemistry
- Nuclear Magnetic Resonance (NMR) & High-Resolution Relaxometry
- Machine Learning / Deep Learning (Convolutional Neural Networks, Signal Processing)
- Molecular Dynamics & Energy Storage Materials
Eligibility & Qualification Criteria
Candidates must meet the following criteria to be considered:
- Academic Background: A Master’s degree (or equivalent) in Chemistry, Physics, Artificial Intelligence, Computational Science, or a closely related discipline. Ideally, candidates should have training in at least two of these domains.
- Technical Skills: Prior experience with programming and coding (e.g., Python, machine learning frameworks) is considered a strong asset.
- Domain Knowledge: Prior practical experience in NMR spectroscopy is advantageous but not mandatory (comprehensive training will be provided).
- Personal Qualities: High curiosity, independence, strong scientific creativity, and enthusiasm for working in an interdisciplinary and international research team.
Supervision and Research Environment
The PhD project will be co-supervised by Dr. Fabien Ferrage and Dr. Guillaume Bouvignies at ENS. The doctoral candidate will benefit from close daily mentorship, state-of-the-art infrastructure, long-standing industrial collaboration with Bruker Biospin, and strong European research networks (HIRES-MULTIDYN and FC-RELAX).
Important Dates
- Application Period: September 1 to October 31, 2026
- Eligibility Check: November 2026
- 3i Committee Evaluation: December 2026
- Shortlisted Candidate Interviews: January 2027
- Project Start Date: March 1, 2027
How to Apply
Interested candidates can submit their applications through the PRISM portal or review the formal posting on EURAXESS. Prospective applicants are also strongly encouraged to contact the supervisors directly via email to discuss their candidacy.
- Supervisor Email: Fabien.Ferrage@ens.psl.eu
- Co-Supervisor Email: guillaume.bouvignies@ens.psl.eu
- Official Application & Details: PRISM PSL Project Portal
- EURAXESS Job Reference: Euraxess Offer #462101
