Postdoctoral Position in Atomistic and Machine-Learning Modelling of Aluminum Combustion

Location: LAAS-CNRS, Toulouse, FranceDuration: 16 monthsThe 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. The questions raised are of great scientific and technological interests and should lead to impactful results. The post-doc researcher will be fully involved within a highly multi-disciplinary team dealing with multi-scale modelling (physics, chemistry and fluid dynamics) working on the development of multifunctional & performance-tailored nanomaterials for energy applications. Position is in Toulouse (LAAS-CNRS laboratory), under the supervision of A. Estève and C. Rossi. Required Education and Experience - A recent PhD degree (within last three to five years) in Materials Science, noticeably Chemistry or related disciplines is required. We seek for a strongly motivated student with strong background in computational materials sciences, with skills in manipulating Density Functional Theory codes (VASP and LAMMPS mandatory) and MLP generators. The applicant should send a detailed CV, including a list of publications and communications and a motivation letter to aesteve@laas.fr.