There is an immediate opening for a staff scientist at Sentient Science in the area of AI and machine learning (ML) guided materials discovery and design. Sentient Science provides digital twin technology to predict the life of mechanical systems. Sentient's DigitalClone® SaaS solutions use proprietary physics-based modeling and machine learning AI to unlock insights that lower the costs of designing, operating, and sustaining high value assets. 

The staff will work on applying ML and AI for developing physics based simulation models, inverse design of materials and for integrating atomistic simulations with X-ray/microscopy for better interpretation of experimental data. Application areas include energy storage, tribology, and catalysis. We are specifically looking for motivated graduate students/early-stage researchers, with interest in AI and machine learning guided modeling of materials. Expertise in molecular dynamics simulations and/or density-functional theory (DFT) for reactive simulations will be considered a plus.

The staff will work in a dynamic team at Sentient Science as well as staff scientists at Argonne National Laboratory. Within the team we have extensive experience with molecular dynamics simulations, reactive empirical force fields, chemical dynamics, numerical algorithms, data analysis, experimental characterization and imaging. Our research has involved methodology and algorithm development in conjunction with extensive applications in the fields of nanoscience and energy-related materials. The project will also involve significant interactions between the staff scientists at various divisions (NST, APS, MCS) within Argonne.

Interested candidates should contact Drs. Subramanian Sankaranarayanan ([email protected]) and Harpal Singh ([email protected])