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Field
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of manufacturing processes Experience modeling AM microstructure evolution, phase transformations, or defect generation Experience with multi-scale modeling across spatial and temporal domains, including residual
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are uncertain, and decisions about where to survey unfold sequentially under significant time and cost constraints. Existing predictive models provide useful but incomplete support because they cannot fully
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, analysing the data using Multi-Level Modelling techniques, and contributing to dissemination and impact activities. You will have a PhD in a relevant field, experience of independent research, and
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-term memory, spatial cognition, legal psychology, and judgment and decision making. Areas of particular interest include attention, perception, working memory, cognitive control, psycholinguistics, and
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for PhD Researcher positions in the area of quantitative, historical and archaeological urban research. The particular focus of the position will be on analyzing urban patterns and processes across large
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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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cell immunology, mucosal immunology, and cancer immunology using animal models and single-cell multiomics/spatial transcriptomics approaches. This postdoctoral researcher will combine wet-lab molecular
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of the position Complete doctoral education leading to a PhD degree. Identify case studies where risk and vulnerability intersect and collect relevant spatial and non-spatial datasets. Co-create vulnerability
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to their work. About You We encourage applications from individuals with a wide range of backgrounds and experiences. You should demonstrate: Essential Criteria: Have a relevant PhD (or equivalent) in fungal
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-environment-climate public goods (AECPG). By combining insights from results-based, collective and spatially targeted schemes with novel financing and robust monitoring, REWARD seeks to build scalable models