Scientific Machine Learning to Simulate Complex Physics Processes in Porous Materials

King Abdullah University of Science and Technology:
Postdoc Positions:
Physical Science and Engineering Division (postdoc):
Energy Resources and Petroleum Engineering (Postdoc) Location

King Abdullah University of Science and Technology (KAUST)

The design of industrial membrane separation materials requires advanced computation methods, such as computational fluid dynamics and computational chemistry, to design, analyze, and predict the properties and performance of these materials. This area also requires experimental studies by providing insights from molecular levels to continuous pore scale level. The modeling of porous membrane usually requires computationally expensive modeling approaches such as molecular dynamics simulations (MD), computational fluid dynamics (CFD) to understand how porous structures and operating conditions can impact membrane performance.

Therefore, this objective of this research is develop efficient algorithms and models based on deep learning to accelerate the physics simulation for membrane relevant processes, which can be based on physics-informed neural network, data-driven models, and hybrid simulation models. These developed models can ultimately be deployed for industrial applications of membrane design and manufacturing processes.

The successful candidate will be based at Inorganic Membranes Research Group at KAUST, under the leadership of Professor Zhiping Lai, and will collaborate closely with Professor Bicheng Yan (KAUST).

Applications are sought for a one-year postdoc position (extendable). The position will include a competitive salary based on the candidate’s qualifications; benefits include medical and dental insurance, free furnished housing on the KAUST campus, annual travel allowance to visit home country, annual paid vacation, and other generous benefits.

Education:

• A Ph.D. degree in computational mathematics, computational mechanics, computational chemistry, machine/deep learning or closely related fields.
• The candidate must have completed all Ph.D. requirements by commencement of the appointment and be within 5 years of completion of the Ph.D.

Experience:

• Experience in computational mechanics, computational chemistry for membrane materials.
• Experience with code development.
• Publications in refereed journals and record of successful research in a collaborative team environment.

Desired

Qualification:

• Experience with Python, Julia.
• Experience in scientific machine learning, deep learning and computer vision.
• Experience in developing physics simulation models with FEniCS, COMSOL, Ansys.

Application Requirements:

• Cover letter highlighting your research experience and justification to fit this position.
• List of publications.
• Copy of your official Ph.D. degree.
• Three contacts of reference.
• Indication of your earliest available date.