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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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format. This will allow combinations of neural networks with physics models. The project brings together PhD students and senior researchers from multiple disciplines to tackle challenges in sustainable
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across infrastructure networks, studying how infrastructure administrators handle uncertainty and complexity across multiple organisational levels when managing their infrastructure assets. The candidate
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sensors, communicating over networks, to achieve complex functionalities, at both slow and fast timeframes, and at different safety criticalities. Future connectivity of the next generation of multiple
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! Education Master's degree (Bac+5) in telecommunications, computer science, or a related field, with an interest in AI, cognitive networks, or connected vehicles. Experience and skills Prior experience with
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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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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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and Advanced Computing Austria (ACA) — you will leverage state-of-the-art infrastructure, interdisciplinary know-how, and national networks. Your results will enhance the AI Factory’s Austria capability
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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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: 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