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with a new cutting-edge quantitative-trading company to push the frontiers of AI-aided decision-making in quantitative trading processes. As a PhD candidate you will: Design next-generation trading
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, this project will Investigate current DES(s) on “controlled” mixed battery chemistries; specifically NMC:LFP ratios. It will also develop an extraction process to optimally recover metals from NMC:LFP black mass
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modelling and simulation of transmission and distribution networks, including benchmarking data models, developing optimal power flow algorithms, and creating state estimation and multi-energy optimisation
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financial barriers to achieving theoretically optimal city sizes using qualitative methods, including stakeholder interviews and policy analysis. Integrating Theory into Dynamic Models Embed the sustainable
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finite element methods, which demand extensive data and are costly, PINNs embed governing physical laws directly into the learning process. This allows effective management of limited and noisy data
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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