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In this PhD position, you will help design the next generation of circular plastics systems by combining hands-on polymer processing with data-driven modelling. The PhD study is a full-time, fixed
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” modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. The most
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-year research and innovation programme developing next-generation circular steels that can safely operate in hydrogen environments. By combining advanced experiments with multiscale modelling, CIRHY aims
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The overall aim of this PhD project is to understand the influence of climate-driven changes in vegetation cover and winter conditions on habitats and food webs of Arctic/alpine lakes. Climate change is
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combining advanced experiments with multiscale modelling, CIRHY enables reliable, sustainable steels for future infrastructure and industry. The project was granted by the Dutch national funding agency NWO in
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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the influence of climate-driven changes in vegetation cover and winter conditions on habitats and food webs of Arctic/alpine lakes. Climate change is promoting the expansion of terrestrial vegetation, which
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hydrogen environments. By combining advanced experiments with multiscale modelling, CIRHY aims to enable reliable and sustainable steels for future hydrogen infrastructure and industry. Hydrogen
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-driven, and grounded-on-Country initiative introduces a transformative model of care that integrates ancient cultural wisdom with modern neuroscience and digital innovation—addressing long-standing health
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flood risk modelling, to answer questions that no existing dataset can address? Are you motivated by the challenge of generating credible extreme flood scenarios that lie beyond the range of historical