10 wave-modeling Postdoctoral research jobs at King Abdullah University of Science and Technology
Sort by
Refine Your Search
-
-inspired approaches for modeling, designing, and predicting the response of composite systems. Responsibilities: Develop AI approaches for predictive multi-physics response of composites in Energy
-
containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling
-
Science and Engineering Division. The Composites Lab started at KAUST in 2009 and is an integrated environment for composite science, combining modeling and experimental expertise in a single working
-
on the development of new methods integrating a variety of data types (remote sensing, geology, geophysics, geochemistry) for geological modelling and advanced exploration targeting of mineral deposits
-
Science and Engineering Division. The Composites Lab started at KAUST in 2009 and is an integrated environment for composite science, combining modeling and experimental expertise in a single working
-
part of the Physical Science and Engineering Division. The Composites Lab started at KAUST in 2009 and is an integrated environment for composite science, combining modeling and experimental expertise in
-
started at KAUST in 2009 and is an integrated environment for composite science, combining modeling and experimental expertise in a single working environment. OUR MISSION: Support Energy transition by
-
. Developing bottom cells at different scales (from 1 by 1 cm2 to 6 by 6 inch2) utilizing the PECVD-PVD cluster. Performing device characterization, and modeling aimed at champion PCEs. Manage project tasks
-
intelligence framework for RO systems. Candidates with background in conventional and innovative membrane-based technologies with data driven modeling approach are encouraged to apply for this position. We
-
healthcare) where deep learning architectures, hierarchical learning models and representation learning can be truly impactful. The group strives to publish in top-tier ML venues such as NeurIPS, ICLR, ICML