Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
integrate complex flow on Discrete Fracture Networks (DFN). The objective of this project is to develop a tool to generate DFN models amenable for multiphase flow, and scale up the model to be usable with
-
project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
-
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
-
departments (Security, IT, Finance, Housing, etc.) to support the onboarding process. • Prepare and update all financial forms required for tenants and work with Finance to initiate and follow up on invoicing
-
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
-
potentially geophysics (inversion) to develop new exploration targeting methods. These will be designed to make the most of the excellent outcrop conditions in Saudi Arabia, which means that a particular focus
-
vacation Additional benefits Remote work possibility About CREST: The Center for Renewable Energy and Storage Technologies (CREST) at KAUST aims to develop renewable energy and storage technologies that help
-
expertise in testing interfaces at the low scale with applications to the energy sector, such as photovoltaic and/or piping systems. Responsibilities: Develop innovative in-situ testing facilities
-
. Develop sensing principles, data acquisition, and measurement systems. Upscale technology and validate its integration into a variety of structures, including structures for mobility, energy, and civil
-
is to develop a modeling framework including the use of Random-Walk method to predict NMR measurements, pore-scale finite-element modeling on 3D digital models, generated from CT-images to predict