Machine-learning interaction potentials for non-spherical nanoparticles

Updated: 15 days ago
Location: Paris 15, LE DE FRANCE
Job Type: FullTime
Deadline: 29 Apr 2026

4 Apr 2026
Job Information
Organisation/Company

CNRS / Sorbonne Université
Research Field

Chemistry
Technology » Materials technology
Physics
Researcher Profile

Recognised Researcher (R2)
Leading Researcher (R4)
First Stage Researcher (R1)
Established Researcher (R3)
Application Deadline

29 Apr 2026 - 22:00 (UTC)
Type of Contract

Temporary
Job Status

Full-time
Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

The aim is to develop machine-learning models that describe how nanoparticles interact with each other, even when they have complex, non-spherical shapes (such as rods, cubes, triangles, or stars). To do this, we will generate interaction data points using macroscopic physical models (including dispersion forces, magnetic effects, and ligand–solvent interactions), and train modern deep-learning methods to create smooth and reliable energy landscapes. A key goal is predict how nanoparticles prefer to attach to each other.  

The machine-learning models will be validated against detailed atomistic simulations and compared with experimental results on self-assembly. Ultimately, this will allow us to predict how complex nanoparticles organize into superlattices.

 

Recent publications related to the project: 

Costanzo, S. et al. Nanoscale, 2021, 12, 24020-24029.

Lahouari, A., Piquemal, J.-P. and Richardi, J. J. Phys. Chem. C 2024 128, 1193-1201


Funding category: Contrat doctoral
PHD Country: France


Where to apply
Website
https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/137718

Requirements
Specific Requirements

The candidate should have a Master (or equivalent) in Theoretical and/or Computational Chemistry or related atomistic/molecular sciences (Computational or Molecular Physics etc.). Speaking French is not required. 


Additional Information
Work Location(s)
Number of offers available
1
Company/Institute
CNRS / Sorbonne Université
Country
France
City
Paris
Geofield


STATUS: EXPIRED

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