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observational research (e.g., simulations) and/or lab or field experiments Experience with the analysis (e.g., sequential analysis, multilevel modelling) and interpretation (e.g., conference presentation
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to peer-reviewed academic publications Qualifications Completed undergraduate degree in physics, computer science, machine learning, computational modelling, or similar. About Swinburne University
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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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on animal models of glioblastoma to evaluate the effectiveness of the selected DDS in tumour growth inhibition, survival rate, biocompatibility and brain targeting efficiency. Aims • Develop a novel and
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effective digital platform for analyzing, visualising and interpreting the condition of civil engineering structures. The digital twin models are created to represent the state and behaviour of structures in
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injection into a reservoir saturated by water from core scale up to the numerical cell or a reservoir is a challenge. For immiscible two-phase flow in layer-cake reservoirs, the models for pseudo relative
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PhD project to develop theoretical computations and modelling methods to guide and optimise the synthesis conditions and composition of electrocatalysts. PhD project to develop theoretical
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that are found readily available in the food supply. In animal models, emulsifiers cause inflammation in the gut, similar to that seen in Crohn’s disease. This project aims at investigating if removing emulsifiers
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for dementia and sector-spanning models of care to improve quality of care and quality of life. Dr Ayton has a strong track record in health and social care research and methodological approaches including
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supervisory team from La Trobe University and Sheffield Hallam University, gaining expertise in data-driven performance analysis, statistical modelling and machine learning. This research will enhance our