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of geometric flows to resolve important geometric and topological questions, and produce new theoretical tools applicable to similar systems arising in areas such as image processing, finance and material
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into neural networks. PINNs can model real-world signals with sparse, non-uniform, and noisy data. A key question is determining the optimal method for integrating physical priors into neural networks
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the optimal habitat reconstruction for declining bird species, how to best engage community and landholders in the recovery effort, and priority species-specific objectives for guiding restoration actions and
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@rmit.edu.au Please send your CV to akram.hourani@rmit.edu.au Required Skills: Programming and simulation: strong experience in Python or MATLAB. Mathematical modelling: probability, optimization, or multi-agent
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to simulate sewer networks as dynamic systems, targeting ≥90% modelling accuracy. Train an explainable decision-making agent to optimize interventions (e.g., pipe upgrades), balancing cost, equity, and
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system (DDS). Based on the existing findings on BBB, develop and optimize DDS for itraconazole delivery. Potential DDS options are liposomes, polymeric nanoparticles, and inclusion complex formation. DDS
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inorganic AEMWEs by integrating the perovskite electrolyte with perovskite electrodes from cell fabrication to cell configuration optimization and to single cell performance. Significance The project has
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to: - Developing underwater communication systems using deep learning which are well-performing to nonlinear channels. - Establishing a deep learning architecture which is optimal for underwater acoustic
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, in the Faculty of Business & Law at Curtin University, is seeking one (1) doctoral applicant (PhD) to join an Australian Research Council [ARC] Discovery Project, namely: Optimizing Benefits
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mechanisms involved in the leaching of copper concentrate by glycine solutions. • Optimize process parameters such as temperature, pH, glycine concentration, and particle size to maximize copper