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environments. Access proprietary market data and institutional infrastructure, including cutting-edge computing environments and live market simulation tools. Translate research into production, contributing
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literacy and navigation in post-sepsis care Digital tools and technology-enabled follow-up Nurse-led models of care The successful candidate will join a dynamic and collaborative environment under
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numerical modelling framework to simulate how light and heat interact with the target body tissue, while also incorporating neural signalling dynamics to explore how light-based stimulation affects
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
of the Bacterial Cell Envelope – From Molecular Structure to Metabolic Function: The cell envelope is the interface between a bacterial cell and its environment. MACSYS is currently modelling the structure and
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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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interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning. PhD Student A will work with tumour sections to develop multiple
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children. Mechanistic modelling of disease transmission involves the use of computer code to represent the epidemic dynamics of infectious disease spread within the community. This allows modellers
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university research into commercial outcomes. Under this program, PhD students will gain unique skills to focus on impact-driven research. This Project aims to develop a predictive machine learning model
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to integrate floating offshore wind turbines and wave energy converters to capture wind and wave energy. The hybrid system’s dynamic responses will be investigated and advanced vibration control technologies
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs