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models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available
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Number req25611 Department Space 4 Center Department Website Link https://s4.arizona.edu/ Location Tucson Campus Address Tucson, AZ USA Position Highlights The Space4 Center at the University of Arizona
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; and be central to demonstrating that autonomous materials discovery can produce real improvements in photovoltaic performance and stability. The full job description is available, here: https
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Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of dynamic sequential inference
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computers lack such abilities. The goal of the Adaptive Bayesian Intelligence Team is to bridge such gaps between the learning of living-beings and computers. We are machine learning researchers with
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dynamics and behaviour, e.g. aerial/drone surveys, line transects, camera surveillance and photo-ID. Experience with Bayesian statistical modelling Proven ability to handle large ecological datasets and
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dynamics and behaviour, e.g. aerial/drone surveys, line transects, camera surveillance and photo-ID. Experience with Bayesian statistical modelling Proven ability to handle large ecological datasets and
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Expertise in machine learning and Bayesian statistics is beneficial Experience in project management is advantageous What we offer: Work on a scientifically exciting, socially highly relevant, and globally
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Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of dynamic sequential inference and probabilistic
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-country survey datasets for comparative analysis. Conceptualize and refine a theoretical framework integrating intersectionality and stigma processes. Develop and code a Bayesian meta-regression to pool and