Job Description

• Apply AI/ML models and screening algorithms for the accelerated design and structural discovery of novel porous materials (e.g., MOFs/COFs);
• Conduct laboratory synthesis, activation, and optimization of the computationally predicted porous frameworks;
• Evaluate CO2 capture performance through experimental gas adsorption isotherms and breakthrough separation testing;
• Manage data pipelines between computational and experimental workflows to support closed-loop project deliverables.

Qualifications

• Bachelor’s or Master’s degree in Chemical Engineering, Materials Science, Chemistry, or computational disciplines with relevant research experience;
• Practical experience in programming (e.g., Python) and applying AI frameworks or molecular simulations to materials discovery;
• Hands-on experience in the synthesis and characterization of porous materials using techniques like PXRD, BET, and TGA;
• Solid understanding of gas adsorption fundamentals and execution of gas separation performance evaluations.