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transformative approach that integrates advanced digital and quantum twin technologies with cybersecurity. This program is a cohort-based, industry-driven, multi-disciplinary graduate training program that aims
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biopsies and advanced, preclinical models. A combination of wet-lab and computational biology, close ties to the clinic, and a wonderful team of early career scientists give us the agility and expertise
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reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl
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suitably qualified candidates for a number of different full-time funded PhD scholarships based at different universities across New Zealand, to start before 31st December 2025. Successful candidates will
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be
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nurseries, understanding how species are affected by different forest management practices, and modelling how conservation and timber production can be combined over time. The PhD students will be employed by
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pa Fixed term Ref: 095093 You will join the group of Professor Alessandro Troisi based in the Materials Innovation Factory (https://www.liverpool.ac.uk/materials-innovation-factory/ ) to work on a
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, traditional risk prediction models like the Steno Type 1 Risk Engine fail to account for the immunological dysregulation inherent in T1D. Project Objective The PhD candidate will primarily focus on the clinical
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frequencies of different UAV categories for different power settings shall be modelled. Second, based on mesoscale fluid dynamic simulations, microclimatic and turbulence modelling procedures in urban
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superstructure that lies at the base of cilia and allows selective diffusion and transport of proteins and lipids between cytosol and cilia by an unknown gating mechanism. By employing an integrative approach