11 bayesian-object-tracking positions at King Abdullah University of Science and Technology
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stakeholders, the role ensures high-quality curriculum design, alignment with emerging technologies, and impactful program outcomes that contribute to national talent development and lifelong learning objectives.
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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
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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
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in the design, processing, and characterization of advanced semiconductors — including organic, hybrid, and 2D materials. Applicants should demonstrate a strong track record of fundamental and applied
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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be considered. Successful candidates should hold a Ph.D. in Biological Sciences or a related field, with a robust track record of publications in top-tier journals, and must be eager to pioneer
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on objectives and timelines. Possibility of some remote working depending on project requirements. Additional Information: Multiple opportunities to learn and use various equipment and techniques. Availability
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. This technology has enabled major advances in the oil & gas industry related to exploration, reservoir characterization, and management. However, its capability is not fully exploited. The objective of this project
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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
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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