Research theme area:
Fuel Cells, Ethanol, Electrolytes, Ion Conduction, Transport Properties, Mechanical Properties Machine Learning, and Atomistic Modeling.
Abstract:
The candidate will collaborate with researchers from the project 83 of the FAPESP-Shell Research Centre for Greenhouse Gas Innovation of POLI-USP at the University of São Paulo. Summary of the program and projects can be found at the RCGI website (http://www.rcgi.poli.usp.br/). A successful candidate will employ machine learning to perform material screening based on first-principles calculations and experimental data to select electrolyte and electrode materials and subsequently describe their mechanical and transport properties through atomistic modeling.
Description:
The applicant will contribute in line with the main objectives of the project:
- Employ advanced computational techniques and machine learning algorithms to identify and assess materials suitable as electrodes and electrolytes in direct ethanol solid oxide fuel cells, including both proton and oxygen ions.
- Conduct molecular dynamics simulations to describe and calculate the physical and mechanical properties of the systems.
- Collaborate closely with a multidisciplinary team of researchers to integrate your findings into the experimental development of solid oxide fuel cells running on ethanol.
Requirements to fill the position:
This project would be well-suited to a highly motivated candidate requiring Programming skills,
experience in machine learning and molecular dynamics and proficiency in English are required.
- The postdoc candidate should hold a PhD in Mathematics, Physics, Computation, Materials Science or Engineering.
INFORMATION ABOUT FELLOWSHIP:
This Postdoc fellowship is funded by FAPESP. The fellowship will cover a standard maintenance stipend of R$ 9.047,40 (Reais) per month.
MORE INFORMATION:
https://sites.usp.br/rcgi/opportunities/
Position: Post-Doctoral Fellowship REF: 24PDR270
Access here AND APPLICATION AT REF Post-Doctoral REF.:24PDR270
