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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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Overview: As data becomes more accessible, new challenges arise around how best to use it—especially in complex, multi-system environments like aerospace. Ontologies offer a powerful solution by
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Project descriptionAutonomous systems are intelligent agents—such as robots, vehicles, or drones—that can sense their environment, make decisions, and act independently. When multiple such agents
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the genetic factors influencing changes in brain structures, using brain imaging, computational and statistical methods of network science. Project Aim: The aim of the project is to uncover the complex
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-time 65%) in the DFG-funded Integrated Research Training Group (RTG) Beyond Amphiphilicity – RTG 2670: Self-Organization of Soft Matter via Multiple Noncovalent Interactions . The position is funded from
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TV-L, part-time 65%) in the DFG-funded Integrated Research Training Group (RTG) Beyond Amphiphilicity – RTG 2670: Self-Organization of Soft Matter via Multiple Noncovalent Interactions . The position
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, Manchester, Sheffield, Monash (Australia) and Temple (USA). The centre is the first of its kind to study how vulnerabilities – such as exploitation by country lines drug networks, online child sexual
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framework to enable meta-analysis of multiple large biobank datasets, all of this with the aim of increasing our sensitivity to unravel the complex genetic causes of disease and, in so doing, identify new
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the need for sustainability to achieve Net-Zero goals. Cyber-Physical Systems (CPS) integrate machines, robots, and AGVs, but challenges like mechanical wear and electronic errors pose risks to efficiency