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Field
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the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and
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management platform that connects institutes to facilitate a rapid and efficient exchange among experimental and computational groups Devising an approach in invertible predictive modeling that links
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models considering networks of patches and their species and interactions composition to predict spatial and temporal community structure across restoration gradients, aimed at developing a predictive
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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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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of work as a case study, this PhD will contribute new knowledge to the fields of archival and performance research, generating a model of practice that can be utilised by other artists. Structured over
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complementary methodologies (corpus data and offline experimental measures). On the theoretical side, the project will develop a formal compositional model that generates the observed parameters of variation and
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main research topic for this position is to investigate the thermal break-up of pure polymers and composites using a combination of experimental techniques and chemical modelling. For a position as a
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, composition, and structural complexity. The group integrates Safe and Sustainable by Design (SSbD) and green chemistry principles from early development stages to guide responsible innovation across materials
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isotope release, and apply advanced characterisation techniques to quantify both released and retained tritium. • Diagnose failure modes in LLZO electrolytes and tailor material composition and