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for estimating sustainable city size by identifying key determinants (e.g., accessibility, agglomeration, emissions, equity), testing spatial boundary sensitivity, and broadening sustainability
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and hands-on experience with AI and computer vision. Solid programming skills in Python, especially with PyTorch. Practical experience with deep learning projects, including working with attention
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) Atomistic classical simulations of macromolecules (v) Biophysics. You can write computer codes to solve some of the daily research problems and have experience with high performance computing. You should have
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) and computer simulation (FEA) Experience in material characterisation and experimental testings Knowledge in impact dynamics Passionate and have interest in pursuing PhD degree. Experience in research
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to estimate their age, but if resorption occurs, it would lead to underestimation and inadequate conservation efforts. Additionally, investigating the role of mononuclear clastic cells in resorption during
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an ARC Linkage Project focused on developing an autonomous system for detecting and quantifying structural damage in infrastructures (e.g., bridges, grain silos) using computer vision, digital twins, and
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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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of generative AI. Essential Skills and Experience A background in a relevant field such as behavioural science, cognitive science, data science, psychology, human-computer interaction, law, or a related
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these constraints into the training objective, complicating model training. This project aims to leverage advancements in computer vision, particularly in implicit neural representations, to embed priors in neural
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qualifications in psychology, human factors, artificial intelligence, human computer interaction, or a discipline that could shed light on individual and team dynamics within the context of command and control