We are looking for a talented and motivated individual to join the "Bridging Models at Different Scales To Design New Generation Fuel Cells for Electrified Mobility (BLESSED)" project. In this role, you will have the exciting opportunity to work on the simulation of critical materials for Proton Exchange Membrane Fuel Cells using ab initio simulations and machine-learning techniques.

Offer Description

The Doctoral Candidate will be hired for two consecutive 18-month periods as part of the “Bridging Models at Different Scales To Design New Generation Fuel Cells for Electrified Mobility (BLESSED)” project which is funded through the Horizon Europe Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks. The individual project will be realized at: (a) the R&D Division of Matgenix in Charleroi, Belgium, under the supervision of Prof. G.-M. Rignanese and Dr. D. Waroquiers, and (b) the Department of Chemistry of the University of Crete (UoC) under the supervision of Prof. G. Froudakis. The successful candidate will be enrolled in the PhD program of the University of Crete, Greece. A one-month secondment at the Statistics section within the Mathematics Department at Imperial College in London/UK is foreseen.

Microporous layers (MPL) is known to improve the PEMFC (Proton Exchange Membrane Fuel Cell) efficiency, especially under wet operating conditions but explanations for this improvement are still under debate, with the process being not yet fully understood. This hinders the development and optimization of new MPL compositions with superior performance. The objective of the PhD project is to improve the efficiency of MPLs, especially under wet operating conditions, by developing atomistic models of MPL materials and performing ab-initio MD simulations of the water-MPL surface interactions.

To this end, the successful candidate will (i) develop realistic atomistic models of MPL materials with different compositions to perform ab-initio molecular dynamics (MD) simulations of water molecules on MPL surface models, (ii) construct a DB of MPL materials and compositions with different model systems at different scales, (iii) validate a multi-scale methodology combining ab-initio simulations with machine-learning (ML) to perform classical MD on large water-MPL surface models, (iv) better understand the performance improvement brought up by MPL in PEMFC systems, (v) identify the important parameters of compositions and porosities to propose new candidate MPL systems with potentially better performance.

To learn more about the project and how to apply, please visit our job posting on EURAXESS:
https://euraxess.ec.europa.eu/jobs/111541

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