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Field
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. The project combines quantitative modelling of markets and the wider energy system with empirical analysis of how flexibility is created, priced, and traded by market participants. A central objective is to
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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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large spatial and temporal scales. Experience in the conceptualisation and development of new methodological approaches for analysing and modelling species range dynamics, functional trait variation
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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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-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
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. Existing predictive models provide useful but incomplete support because they cannot fully capture the complexity and heterogeneity of archaeological data. This underscores the need for approaches
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utilizing animal models and the downstream isolation and analysis of immune cells using a variety of techniques including, but not limited to, flow cytometry, ELISA, RT-PCR, bulk and single cell RNA
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). Experience in transgenic animal models and/or in vivo interventions and disease models. Desirable Criteria: Experience with emerging technologies e.g. ‘omics and advanced informatics, spatial transcriptomics