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Field
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recirculating flumes, velocity measurement systems, spectroscopic tools, and numerical modeling platforms, to investigate hydrologic and sediment processes. Works closely with USGS scientists and University
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area of Gaussian Process based Simultaneous Localisation and Mapping (SLAM) and recent advances in non-parametric, 3D environment modelling. We will assess the utility of these tools for map-building
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/knowledge: Experience in lab or synchrotron x-ray reflectometry techniques and fitting data to existing models. Interest or experience in instrumentation development. Experience in condensed matter material
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to more than one; state your preference in your motivation letter. 1. Neurodevelopmental proteomics You will map the molecular circuitry of human neurodevelopment using advanced 2D and 3D cell models together
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aimed at the modelling and analysis of complex phenomena particularly within economics and finance, energy finance, sustainable and ethical economics and insurance. We are interested in research projects
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approaches including stable isotope techniques, omics-based methodologies and quantitative modelling to understand nitrogen cycling within soil-plant systems under rehabilitation and development. Publish
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with experience. • Demonstrated research experience in Modelling and Simulation (M&S) and decision support analytics, stakeholder engagement and an active research agenda evidenced by research, supported
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, and workforce development, in addition to advanced instrumentation, including recirculating flumes, velocity measurement systems, spectroscopic tools, and numerical modeling platforms, to test new
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of generative AI and biophysics, the Fellow will focus on expanding the current framework to model dynamic protein ensembles. As an Empire AI-funded fellow, you will have access to the Empire AI Alpha and Beta
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. Desirable (but not essential): Prior experience or demonstrated interest in economic input–output analysis, including multi-regional input–output (MRIO) frameworks or supply-chain modelling. Prior experience