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Field
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candidate will contribute to feasibility studies on hybrid propulsion systems, the development of robust control architectures for autonomous docking, and predictive maintenance strategies using real-time
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Fully Funded PhD Research Studentship tax-free stipend of £20,870 Design, Informatics and Business Fully Funded PhD Research Studentship Project Title: Autonomous AI-powered red agents for enhanced
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materialists, electrical engineers, and computer scientists of TUD, RWTH Aachen and Gesellschaft für Angewandte Mikro- und Optoelektronik mbH (AMO ) in Aachen, Forschungszentrum Jülich (FZJ ), Max Planck
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architectures, and on-device inference on edge-compute platforms. Demonstrated analytical problem-solving through experimental design, critical quantitative and qualitative data analysis, and validation
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of code to utilize GPU-acceleration on DTU’s high-performance computing cluster or other HPC systems. You will also analyze realistic physical implementations of the architectures you explore, with a
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understanding of how genomic architectures drive soil carbon sequestration and will highlight microbial biodiversity suited to specific soils and climates, paving the way for more sustainable soil management
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of diverse, deformable textiles at cycle times below one second, while hyperspectral, NIR, Raman, and RGB sensors feed an edge-compute AI pipeline for real-time decision making that routes each item
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for these architectures. This will also guide hardware design for such devices and tackle crucial challenges in networked systems and entanglement transmission. Candidate’s profile Knowledge of quantum computing and an
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admission to the PhD programme in Architecture within three months of your employment contract start date, and that you participate in an organized doctoral programme throughout the period of employment As
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operational employment. This doctoral research will thus leverage the power of graph neural networks – a novel ML architecture, capable of learning fundamental physical behaviour by modelling systems as graphs