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written and verbal communication skills, experience with developing and implementing Bayesian statistical models, and be proficient in computer programming in e.g. R or Python, and C/C++. Please ensure you
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the appropriate area. Familiarity with statistical analysis software (e.g., STATA, R, SPSS) or computer programming (e.g. C++, Python, R) and experience working with health-related data will be advantageous
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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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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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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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. 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