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(Global Petroleum Gas) Materials Modelling & Data Science Specialist
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(Global Petroleum Gas) Materials Modelling & Data Science Specialist

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1 Vacancy
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Jobs by Experience

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5+ years

Job Location

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Dhahran - Saudi Arabia

Monthly Salary

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Not Disclosed

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Salary Not Disclosed

Vacancy

1 Vacancy

Job Description

Req ID : 1807518
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.

Duties & Responsibilities:
You will be required to perform the following:
  • Develop computational models to simulate catalytic reactions, model a variety of catalytic systems (e.g. metals, metal oxides, and zeolites), develop fundamental understanding for these complex reaction systems, and propose new catalysts to overcome the limitations of existing catalysts.
  • Use state-of-the-art machine learning techniques to develop surrogate models to propose new catalysts based on previous experimental results with the goals of accelerating the timeline for materials discovery and optimizing the outcome of the catalytic process (e.g. yield, conversion, and process conditions).
  • Remain updated with the current and fast evolving field, be able to publish technical findings in international peer reviewed journals and spread awareness of the latest tools and techniques in the organization.
  • Oversee and provide technical leadership to a team of junior scientists and engineers, and be responsible for the training and development of junior mentees.
  • Organize and lead teams for specific projects and tasks, and manage discussions with internal and external project teams.

Requirements

Minimum Requirements:
  • PhD in Chemical Engineering, Chemistry, Physics, Materials Science or a related field from a recognized institution. Strong background in catalysis, DFT and data science.
  • A robust theoretical background in computational catalysis using density functional theory (DFT), microkinetic modeling, data science, and strong software development skills. Exposure to process engineering is preferred.
  • Ability to present theoretical interpretation to experimental observations, using theoretical background to predict new and improved catalysts, and use data science to generate surrogate models to propose new catalysts based on limited experimental data.
  • At least five years 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 a requirement.
  • Experience with process simulations and techno-economic evaluations is preferred. Granted patents based on novel research results is also preferred.

Benefits

Our high-performing employees are drawn by the challenging and rewarding professional, technical and industrial opportunities we offer, and are remunerated accordingly.

Minimum Requirements: PhD in Chemical Engineering, Chemistry, Physics, Materials Science or a related field from a recognized institution. Strong background in catalysis, DFT and data science. A robust theoretical background in computational catalysis using density functional theory (DFT), microkinetic modeling, data science, and strong software development skills. Exposure to process engineering is preferred. Ability to present theoretical interpretation to experimental observations, using theoretical background to predict new and improved catalysts, and use data science to generate surrogate models to propose new catalysts based on limited experimental data. At least five years 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 a requirement. Experience with process simulations and techno-economic evaluations is preferred. Granted patents based on novel research results is also preferred.

Employment Type

Full Time

Company Industry

About Company

0-50 employees
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