The successful candidate will join the GREENTEA project, a national consortium bringing together nine academic partners working at the interface of materials chemistry, robotics, advanced characterization, and artificial intelligence. The project involves close collaborations with CEA Paris-Saclay and the European Synchrotron Radiation Facility (ESRF).
Main mission
The successful candidate will have to develop and implement high-throughput solution-based synthesis methods for sulfide thermoelectric materials. He/she will develop synthesis protocols enabling the rapid and reproducible exploration of a large number of compositions, relying in particular on solution chemistry and automated high-throughput synthesis approaches.
The position will involve optimizing synthesis conditions, preparing composition libraries, performing their structural and chemical characterization, and analyzing the resulting data to identify the most promising compositions and synthesis conditions. The successful candidate will work closely with the teams involved in materials modelling, artificial intelligence, and materials property characterization, contributing to an iterative workflow linking prediction, synthesis, and characterization.
Activities
- develop high-throughput solution synthesis protocols;
- perform structural characterization using WAXS and synchrotron X-ray techniques;
- use machine learning and Bayesian optimization to guide synthesis and composition selection;
- scale up the most promising materials for complete thermoelectric characterization;
- share results (high-impact scientific publications and presentations);
- supervise graduate/undergraduate students and active participation in the GREENTEA collaborative network.
The net monthly salary is between €2,200 and €2,380
The contract duration is 15 months
Requirements
Research Field: Chemistry
Education Level: PhD or equivalent
Skills/Qualifications
- inorganic solution synthesis;
- solid-state chemistry or functional materials;
- X-ray diffraction and structural characterization;
- thermoelectric materials; (desirable but not mandatory)
- machine learning or data analysis; (desirable but not mandatory)
- scientific programming (Python). (desirable but not mandatory)
Additional Information
Selection process
Applicants should send a CV, a cover letter, a publication list, contact details of two or three referees (or recommendation letters) to mickael.beaudhuin@umontpellier.fr
