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This project will investigate artificial intelligence (AI) to improve weather forecasts and use crop models for making better farming decisions. The expected outcome is protocols for integrating AI
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reactivity under conditions relevant to top gas recycling BF with hydrogen gas injection, utilising a range of experimental, analytical, and kinetics modelling techniques. The ideal candidate would have a
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city size framework into dynamic land use–transport interaction (LUTI) models to enable scenario-based forecasting and planning under different infrastructure and policy conditions. Understanding
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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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Martin Australia invite applications for a project under this program, exploring the development of Physics Informed Neural Networks (PINNs) for efficient signal modelling in areas such as weather
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Optimizing a 3D microfluidic IVD model to study cell responses to wear particles, refining culture conditions, and analysing cytotoxic and inflammatory mechanisms. Optimizing a 3D microfluidic IVD
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. This position will focus on one key stream: understanding and improving the interaction between EVs and roadside barriers. This will involve: Modelling EV crash dynamics with conventional and SRB barriers
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) to simulate sewer/stormwater system behaviour, predict risks, and optimize interventions. GNNs act as surrogate digital twins, embedding hydraulic principles to model how land-use changes and extreme weather
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frameworks that model the users who are going to interact with them. These models are typically a simplified representation of users (e.g. using the relevance of items delivered to the user as a surrogate for
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underwater communications is necessary. Conventional approaches in underwater communications only develop fixed models based on human knowledge or understanding which cannot fully cover the highly dynamic and