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programming techniques (e.g., techniques for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference
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to contribute to sustainability transitions. You explore how these collectives and their networks reach beyond their immediate communities, and how they can build the infrastructures needed for large-scale
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this PhD position, you will study how citizen collectives can mobilise broad groups of citizens to contribute to sustainability transitions. You explore how these collectives and their networks reach
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to strengthen your research expertise and expand your professional network. Additionally, you will have the opportunity to contribute to teaching and supervise AI-related thesis projects at both
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can be dominated by either mineral dissolution (forward weathering) or by secondary mineral precipitation (reverse weathering). The net marine CO2 drawdown and element turnover related to marine
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
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for professional growth through participation in consortium activities, international summer schools and workshops, enabling you to strengthen your research expertise and expand your professional network
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gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques); giving mathematical proofs of their correctness and efficiency; building state
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(e.g. safety, norms, networks) influence active lifestyles; develop and test physical activity and destination choice models, integrating data from sources such as sensors, surveys, Google Street View
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, environmental stressors (e.g. heat, air pollution, pollen), and social factors (e.g. safety, norms, networks) influence active lifestyles; develop and test physical activity and destination choice models