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state estimation, this project invites you to transcend these boundaries—developing novel, computationally efficient models grounded in real-world data, and leveraging RL to optimise performance, lifetime
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond
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metapopulation and/or individual based models Knowledge of Bayesian methods, including Approximate Bayesian Computation Experience with big data analysis and HPC environments Knowledge of additional programming
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can be tackled. A video describing the project can be viewed here: https://www.youtube.com/watch?v=IzPuuBnrIDc . The successful candidate will be developing Bayesian models for estimating
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mortality data from diverse sources and estimate fine-scale geographic and socioeconomic inequalities in under-five mortality rates across all major cities in Ghana, and assess how these inequalities have
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. Probabilistic rational models, implemented as either Bayesian models or deep neural networks, have been proposed as standard models, from low-level perception and neuroscience to cognition and economics. But
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approaches. Experience in programming in R, using GitHub, and doing Bayesian statistical analyses with the use of MCMC samplers such as JAGS, STAN, or NIMBLE. Point of Contact Justina Eligibility Requirements
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The Computer Vision-Core Artificial Intelligence Research (Vision-CAIR ) group led by Prof. Mohamed Elhoseiny at the CS Program of the King Abdullah University of Science and Technology (KAUST) is
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learning, Large Language Models, Stochastic optimization, Transfer & Evolutionary optimization, Bayesian optimization for complex design in material and engineering. Key Responsibilities: Collect relevant
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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods