About the Company
This company engages in the exploration, production, transportation, and sale of crude oil and natural gas. It operates through the following segments: Upstream, Downstream, and Corporate. The Upstream segment includes crude oil, natural gas and natural gas liquids exploration, field development, and production. The Downstream segment focuses on refining, logistics, power generation, and the marketing of crude oil, petroleum and petrochemical products, and related services to international and domestic customers. The Corporate segment offers supporting services including human resources, finance, and information technology. The company was founded on May 29, 1933 and is headquartered in Dhahran, Saudi Arabia.
The CCoE is responsible for designing innovative catalysts to convert oil and natural gas into higher value fuels and chemicals, in addition to sustainability catalysts which enable CO2 utilization, low-carbon fuels production, and a hydrogen economy. The candidate will lead multiple projects and tasks associated with Materials Modeling & Data Science as part of a team which will accelerate the discovery of catalytic materials to resolve significant challenges in the hydrocarbon industry, and will complement existing experimental work through theoretical framework.
- Develop computational models for simulating catalytic reactions and catalytic systems, including metals, metal oxides, and zeolites.
- Develop fundamental understanding of complex reaction systems and propose new catalysts to overcome limitations of existing ones.
- Use machine learning techniques to develop surrogate models for accelerating materials discovery and optimizing catalytic processes.
- Stay updated with the latest tools and techniques in the field, publish technical findings in international peer-reviewed journals, and spread awareness of new developments in the organization.
- Provide technical leadership and oversee a team of junior scientists and engineers, and train and develop junior mentees.
- Organize and lead teams for specific projects and tasks, and manage discussions with internal and external project teams.
- PhD in Chemical Engineering, Chemistry, Physics, Materials Science, or a related field from a recognized institution with a strong background in catalysis, DFT, and data science.
- Robust theoretical background in computational catalysis using DFT, microkinetic modeling, data science, and strong software development skills. Exposure to process engineering is preferred.
- Ability to interpret experimental observations using theoretical background, predict new and improved catalysts, and use data science to generate surrogate models to propose new catalysts based on limited experimental data.
- 5+ years of relevant experience.
- Strong record of publications in recognized peer-reviewed journals and presented research outcomes in international events. Outstanding communication skills in English is also required.
- Experience with process simulations and techno-economic evaluations is preferred.
- Granted patents based on novel research results are also preferred.
Skills : DFT,DFT Compiler,Data science,catalysis