Postdoctoral Fellow in the DeepWave Consortium

KAUST; Quezon City; On-site; PHP 2,303,389 - 3,126,028; Full time

Job summary

King Abdullah University of Science and Technology (KAUST) invites two postdoctoral researchers to advance ML methods for wave‑equation based processing, imaging, and inversion. You will join the ESSE group under Prof. Tariq Alkhalifah and work on DAS, subsurface characterization, and physics‑driven ML.

Strong publication record and Python/Torch/JAX skills are preferred. Applicants should have a PhD in a related field, excellent English, and will contribute to open source software and

Qualifications

• PhD in Computational Geophysics, Computer Science, Applied Mathematics or related field.
• Strong publication record in relevant areas.
• Proficiency in Python and Torch and/or JAX.
• Excellent written and spoken English.

Responsibilities

• Develop novel ML methods for wave-equation based processing, imaging and inversion.
• Validate methodologies with field data and real-world applications.
• Publish results in peer‑reviewed journals and present at conferences.

Skills

Python

Torch

JAX

Machine Learning

Deep Learning

Education

PhD in Computational Geophysics

Tools

Git

Job description

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

Location

King Abdullah University of Science and Technology | Thuwal | KSA

Open Date

Feb 25, 2026

Description

The DeepWave industry‑funded consortium is looking for two outstanding postdoctoral researchers to undertake impactful research on the development and application of cutting‑edge machine (deep) learning numerical methods for wave‑equation‑based processing, imaging, and inversion.

Wave phenomena are ubiquitous in science, and they extend to objectives ranging from global Earth discovery, to natural resources exploration, to subsurface monitoring, as well as nondestructive testing and medical imaging. However, our current ability to create detailed images of the interior of such bodies from remote measurements and accurately invert for physical properties often lacks the accuracy and resolution we seek for making informed decisions. Both shortcomings are usually attributed to the limitations in our measurements and in the underlying physical models. Machine learning (ML) techniques can be exploited to identify common patterns in the data and augment the physical laws of wave propagation, leading in turn to improvements in accuracy and resolution.

The selected candidate will join the research group of Prof. Tariq Alkhalifah within the Earth Systems Science and Engineering (ESSE) program at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, and will work closely with other group members. The candidate will be expected to develop novel methodologies, validate their effectiveness using field data, and contribute to scaling these approaches to real‑world applications in one or more of the following areas:

• Machine-learning-assisted subsurface characterization and monitoring
• Distributed Acoustic Sensing (DAS) data processing and compression using ML
• Physics-driven machine learning for geophysical modeling and inversion

Thus, the candidate is expected to have or be about to have a PhD in a relevant topic that includes geophysics, mathematics, physics, computer science or any related topics with a track record in relevant applications (processing, imaging and inversion).

In addition to initiating, developing, and delivering high-quality research, collaborating with students, the candidate will be expected to publish in leading peer‑reviewed journals, present at international conferences, and contribute to improving the quality and efficiency of the consortium code base. Preference will be given to candidates with a strong publication record and proven experience in Python programming, source code versioning and management, machine and deep learning, as well as a solid understanding of wave phenomena and geophysical data analysis and imaging.

Qualifications

Basic Qualifications:

• A Ph.D. in Computational Geophysics, Computer Science, Applied Mathematics or similar field;
• Portfolio of relevant publications;
• Good programming skills in Python and proficiency in Torch and/or JAX;
• Proficiency in written and spoken English.

Differentiating Qualifications:

• Expertise in the development of seismic processing algorithms, high-performance computing, and/or large-scale inverse problems;
• Experience in developing open-source software and a track record in collaborative software development.