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algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable strategies for estimating archaeological potential, that capture distinct criteria including
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layers. Within this environment, we will design and evaluate new reinforcement learning algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable
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. The project will consider different types of epidemic models, including spatial and individual-based models. Methodologically, the work will focus on multi-objective, hierarchical, and explainable reinforcement
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, socioeconomic, and geospatial data; Contribute to patient-level and population-level modelling approaches, including temporal, spatial, and multimodal prediction frameworks; Apply and evaluate methods for disease
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research concerning determination of the concentration and spatial distribution of (ultra)trace elements in materials of both biological and synthetic origin using laser ablation – ICP-mass spectrometry (LA
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systems have identifiable material, structural, or spatial implications. The aim of your work is to contribute to the project by helping to establish a framework for understanding the relationships between
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Experience with digital participation platforms, online surveys or citizen science technologies Experience with participatory mapping or spatial approaches Experience with indicator development, dashboard
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different types of epidemic models, including spatial and individual-based models. Methodologically, the work will focus on multi-objective, hierarchical, and explainable reinforcement learning, as
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state-of-the-art approaches including single-cell and spatial transcriptomics, circuit tracing and connectomics, and automated behavioral analysis. In close collaboration with the Verstreken and de Wit