Atomistic and Machine-Learning Modelling of the Aluminum Combustion (M/F)

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description The way aluminum is reacting with an oxidative atmosphere is of major interest in a number of timely applications, i.e. propellants, energetic materials at large, cyclable green energy production … We are seeking a strongly motivated post-doctoral researcher to work on the modelling of the high temperature Al-O system. The candidate will take in charge DFT and AIMD (ab initio Molecular Dynamics) calculations to complete the set of already available data from our group, to answer specific and mechanistic at the most fundamental level of these systems. He will further develop MLP (Machine Learning Potential) using standard techniques, including active learning procedures. The project has several scientific objectives:. (i) Establish/consolidate a machine learning interatomic potential dedicated to the Al-O system in the Al liquid temperature window,. (ii) Study the thermochemistry of oxide nucleation and growth within liquid aluminum. (iii) Investigate the interaction of alumina aggregates with liquid Al droplets. DFT calculations. Machine learning tools for generating interatomic potentials. Molecular Dynamics. Position is in Toulouse (LAAS-CNRS laboratory), under the supervision of A. Estève and C. Rossi, MRS team, Energy Management department. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UPR8001-ALAEST-007/Default.aspx . Requirements Research Field Mathematics Education