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any prior research and/or relevant work experience Curriculum Vitae Academic transcript Preferable background and/or previous experience or publications in materials science, modelling (across length
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to a wide range of materials, making impactful tools for the scientific community. The models and predictions in the project will be tested against real experimental data and used to drive the design of
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to train an AI model that predicts the cis-regulatory code for synthetic genomes (i.e. for cell-free gene expression systems) and correlates the experimental conditions within the synthetic cell
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geothermal resources, reservoir modeling, techno-economic analysis and machine learning implementation, and other emerging geothermal energy-related topics. The selectee will be expected to develop funding and
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of methane dynamics in rapidly changing ecosystems and contribute to improving predictive models of future methane emissions. Field sampling will focus on regions where methane cycling is still poorly
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electron microscopy image simulations Development of a machine learning model capable of inferring 3D atomic structure from two-dimensional TEM projection images Application of the new approach
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You will explore the nature of metal corrosion inhibitor interactions through advanced molecular modelling and integrate this understanding into the formulation of evolutionary algorithms
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to an imperfect combustion in engines) and those emitted outside the exhaust (linked to the abrasion of tyres and the wear of brakes). The dynamics of exhaust and non-exhaust pollutants released into the atmosphere
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mobility of migrants shape local populations? Developing a spatial microsimulation model of population dynamics with application in infectious disease modelling (DynaMIGs)”. DynaMIGs is a four-year
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, and compatibility with existing fuel infrastructure. Co-fuelling of ammonia with hydrogen in internal combustion engines has been extensively studied because of improved combustion characteristics, e.g