Postdoctoral Fellow in the DeepWave: King Abdullah University of Science and Technology invites applications for the position of Postdoctoral Fellow in the DeepWave Consortium. The DeepWave industry-funded consortium seeks a highly qualified researcher to work on advanced machine learning numerical methods for wave-equation-based processing, imaging, and inversion.
Table:
| Designation | Research Area | Location | Eligibility/Qualification |
|---|---|---|---|
| Postdoctoral Fellow | DeepWave Consortium, Geophysics, Machine Learning, Inversion | King Abdullah University of Science and Technology, Saudi Arabia | Ph.D. in Computational Geophysics, Computer Science, or related fields; programming skills in Python; proficiency in Torch and/or JAX; proficiency in English. |
Job Description: The selected Postdoctoral Fellow will work in the research groups of Prof. Tariq Alkalifah and Prof. Matteo Ravasi within the Earth Science and Engineering (ErSE) school at King Abdullah University of Science and Technology (KAUST). The role involves developing innovative methodologies, validating their effectiveness with field data, and contributing to real-world applications in various geophysical topics. The primary areas of focus include machine learning-assisted subsurface characterization and monitoring, closing the gap between training and testing data, physics-driven machine learning for geophysical modeling and inversion, and other AI-related topics.
Qualifications: Basic Qualifications:
- Ph.D. in Computational Geophysics, Computer Science, Applied Mathematics, or a related field.
- A portfolio of relevant publications.
- Strong programming skills in Python and proficiency in Torch and/or JAX.
- Proficiency in both written and spoken English.
Differentiating Qualifications:
- Expertise in developing seismic processing algorithms, high-performance computing, and large-scale inverse problems.
- Experience in open-source software development and collaborative software projects.
How to Apply: Interested candidates are encouraged to submit their applications by clicking on the “Apply Now” button.
Last Date for Apply: Application Deadline: 31 December 2023, at 11:59 PM Eastern Time
Disclaimer: This job post is based on information available at the time of writing. Applicants are encouraged to verify details, eligibility, and application procedures from the official source.
For further information and to apply, please visit the official website.
For additional inquiries, please contact:
- Prof. Tariq Alkalifah
- Prof. Matteo Ravasi
Please note that this job post is based on publicly available information, and applicants are encouraged to verify details from the official source.
