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This PhD project focuses on strengthening network security for large-scale distributed AI training. As training increasingly spans multiple data centers connected over wide-area networks, it
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candidate to undertake research in the area of cognitive networking for 5G/6G networks, with a focus area on connected and autonomous mobility. This research will explore how artificial intelligence can be
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the evidence–base needed to understand the impact on health, to inform public policy, and to develop potential mitigation strategies. Traditionally, this information has come from ground monitoring networks
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, Austria, and with Chrometra, a Belgian company. By being embedded in the WATER research network, you will also interact with parter groups located across multiple EU research groups, building your research
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represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster networking, enable internationalization and
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actively contribute to this strategic initiative by researching and developing scalable, practical frameworks for implementing Trustworthy AI principles in diverse projects and across multiple technology
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environments—such as fleets with multiple aircraft types. Objectives Objective 1: Map current data types, structures, and interoperability challenges to build a detailed "as-is" understanding of current
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optimising performance across multiple timescales and spatial domains. Systematically resolving these challenges in renewable-dominated power networks stands as a critical cornerstone for enabling the roadmap
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: this provides capability for accurate and fast modelling of urban drainage, handling the full complexity of flow paths on impermeable surfaces, green space, buildings, pipe networks and BGI features
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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training