Scientific Machine Learning to Simulate Complex Physics Processes in Porous Materials

KAUST; Thuwal; On-site; SAR 180,000 - 240,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Housing on KAUST campus

Annual travel allowance

Paid vacation

Job summary

KAUST invites applications for a one-year postdoc position in the Inorganic Membranes Research Group. The role involves developing ML-accelerated physics simulations for membrane materials and collaborating with leading KAUST researchers, with competitive salary and benefits including housing on campus.

The successful candidate will have a PhD in relevant computational fields, strong publication record, and experience with Python, Julia, and scientific ML techniques.

Qualifications

• PhD in computational mathematics, computational mechanics, computational chemistry, machine/deep learning or closely related field.
• Candidate must have completed all PhD requirements by start and be within 5 years of completion.

Responsibilities

• Design and accelerate physics-based membrane simulations using ML methods.
• Collaborate with KAUST researchers in the Inorganic Membranes group to develop efficient models.

Skills

Python

Julia

Scientific machine learning

Deep learning

Computer vision

Code development

Education

PhD in computational mathematics/ computational mechanics/ computational chemistry/ machine/deep learning

Within 5 years of PhD completion

Tools

FEniCS

COMSOL

Ansys

Job description

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)

Description

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.

Qualifications

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.