Sort by
Refine Your Search
-
Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
-
tasks require high-frequency evaluations of forward models, in order to quantify the uncertainties of rock and fluid properties in the subsurface formations. Therefore, the objectives of this research
-
Storage, Hydrogen Economy, Economics, Business Administration, Management, and Finance. Discover more about our research centers and our research strategy. If your research aligns with ours, we would
-
, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
-
industry partners. Design, implement, and validate advanced reinforcement learning models. Utilize reinforcement learning and evolutionary algorithms to discover new chemical materials. Publish and present
-
. These workflows will then be applied in relevant Saudi Arabian contexts to help discover new ore deposits. The position will combine techniques from geological modelling, geostatistics, machine learning, and
-
all aspects of these complex problems. In the Composites Lab, you will find skills ranging from theoretical mechanics, applied mathematics, and computer science to material science and chemical
-
effective solution to mitigate GHG emissions that can be deployed at large scales. CCS may enable the industry to continue operating with reduced environmental footprint. The objective of this project is to
-
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