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medical applications. Federated Bayesian learning offers a solution to those problems by allowing multiple participants to train machine learning models collaboratively, without sharing any data. Bayesian
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redefinition. Architectural discourse has often relied on standardized bodily models that mask the diversity of lived, gendered, and capacitated experiences. In response, this project explores how a multiplicity
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learning, mathematical statistics, optimization, and robotics. Experience from programming in C/C++ or Python is also meritorious. Willingness to work in an inter-cultural, international, and diverse group
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interactions with citizens. The position includes collaboration with researchers across multiple universities and international partners, and will involve empirical studies of AI use in public-sector