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. Measurements on fuel injectors relevant to current design standards have shown significant influence of injector aerodynamics on the dispersed spray distribution and the importance of prefilming fuel flows
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lack a direct correlation with process parameters, limiting their ability to predict temperature fields under varying process conditions. The transferred arc energy distribution becomes particularly
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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distribution of normal cardiac anatomy and function (including motion) from healthy subjects. By establishing an understanding of "what normal looks like", these models will detect deviations from the norm and
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novel opportunity to automate and improve the frailty assessment process, aiming for greater consistency and predictive accuracy. Aims i) Develop a deep learning algorithm to autonomously detect and
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analytics, anomaly detection, and embedded redundancy to enhance system resilience. Students will focus on creating adaptive algorithms and hardware implementations that enable real-time diagnostics and
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CST Microwave Studio, HFSS or EM Pro for antenna modeling and design is required, as is experience with programming languages like MATLAB, Python, or similar for antenna array analysis and algorithm
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and subsequently distribute within them, typically using microscopy-based techniques. The position is part of the national NanoMedNL consortium and the candidate will interact with other PhD candidates
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(HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data