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Postdoc Machine-learning interaction potentials ... (1 reply)
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A two-year postdoctoral position is available to start on October 2021. The project aims at implementing long-range electrostatic interactions and on-the-fly training in machine-learning interactions potentials. The final objective consists in employing these methodological developments to study the nucleation and crystallization in iron oxide nanoparticles.
Your qualifications:
The position is opened to strong applicants with backgrounds in physics, chemistry and/or engineering. Research skills of particular interests include Machine-learning methodology, C++ programming, Molecular dynamics simulations and Software developments.
Supervision and teaching:
Retained candidate will benefit from the supervision of Dr. Julien Lam and Dr. Magali Benoit. Together, their research is focused on material modeling using many types of computational techniques. He/She will gain experience in a large number of research domains, especially machine-learning, atomistic simulations, statistical physics and computer programming. If interested, the hired postdoctoral researcher may also be trained in student supervision (Bachelor, Master and PhD) and proposal writing (Marie-Curie fellowship, CNRS permanent position).
How to apply:
Application documents (CV, motivation letter) should be sent to [email protected] with the following title "Postdoc Application NucleFOX"
References:
-”Perspective: Machine learning potentials for atomistic simulations” J. Chem. Phys. 145, 17,
170901 (2016)
-”Measuring transferability issues in machine-learning force fields: The example of Gold-Iron in-
teractions with linearized potentials” M. Benoit, J. Amodeo, S. Combettes, A. Roux, I. Khaled, J.
Lam Mach. Learn.: Sci. Technol. 2 025003 (2021)
-”Combining quantum mechanics and machine-learning calculations for anharmonic corrections to
vibrational frequencies” J. Lam*, Saleh Abdul-Al, A-R Allouche* J. Chem. Theory Comput. 13,3
(2020)
-“Out of equilibrium polymorph selection in nanoparticle freezing”
J. Amodeo, F. Pietrucci, J. Lam*
J. Phys. Chem. Lett. 11, 8060 (2020)