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the molten pool. However, these models are computationally intensive and impractical for widespread simulations of large-scale part deposition. This project aims to develop a novel FEA-based approach
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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aims at developing tools using large language models (LLMs) for the correction of misinformation about climate change in social media. The successful candidate will develop innovative tools leveraging
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Lazzarini as your proposed principal supervisor, and copy the link to this scholarship web page into question two of the financial details section. About the scholarship Diabetes is the most rapidly growing
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computing. Current challenges in quantum technology adoption stem from the lack of standardized benchmarking methods and the inherent difficulty in validating quantum devices beyond classical simulation
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are linked to research on composite hydrogen tanks, composite propellers for drones and finite element modelling of textile manufacturing. All research will be conducted with leading companies in
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species like birds and bats. Combining PAM with occupancy modeling allows for large-scale ecological studies, assessing both individual species and community responses to environmental changes. In this 4
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on these materials, which will be used to implement high frequency and high efficiency IVRs for HPC applications. Objectives:-Fabrication, characterization and modeling of the composite magnetic materials.-Modeling
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measurements in a team of experts on and in the pyramids and creating digital object models with numerical simulations, for example, using Salvus software or similar. Publication of research results and
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, engineers and PhD candidates. The PhD candidate is expected to develop an advanced engineering noise prediction model for efficient computation of sound propagation in a range-dependent atmosphere where