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to intelligent urban mobility studies, in particular mobility data analysis and modeling. Statistical modeling software and other tools for data processing and analysis will be used. The fellow will also take part
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that preserves reliability and resilience; (3) quantify economic viability and equitable pricing models for energy and ancillary services provided by cVPPs; and (4) train interdisciplinary researchers to support
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Postdoctoral Research Fellow for a highly interdisciplinary and collaborative project. The main objective of the project is to develop multiphysics models for urban CFD in OpenFOAM. At Computational Combustion
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Science, or a related field. • Strong understanding of heat and mass transfer, thermal comfort, and indoor environmental quality. • Proven experience in CFD modelling for airflow and heat transfer
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of next-generation AI agent architecture in complex and dynamic environments. Collaborate with academic and industry partners, including potential joint projects with institutions to drive interdisciplinary
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processes in Australia. The second will involve a climate or biodiversity deliberative assembly based in Europe. Your key responsibilities will be to: document and analyse existing models of MtHR and what is
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vehicles navigating shared road networks, AI agents coordinating in complex games, and human-machine partnerships in online platforms, AI assistants, and physical systems. While multi-agent interactions have
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infrastructure evolves, this position is likely to present diverse and flexible opportunities - from deeper and more complex software design problems, to becoming more involved in the bioinformatic and analytic
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resilience to stressors in fish. The aim will be to integrate technology to develop the knowledge base for understanding stress resilience behaviours in Asian Seabass in farm conditions using laboratory model
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resilience to stressors in fish. The aim will be to integrate technology to develop the knowledge base for understanding stress resilience behaviours in Asian Seabass in farm conditions using laboratory model