40 postdoctoral-soil-structure-interaction-fem-dynamics PhD positions at Cranfield University
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in radiation–matter interactions, computational modelling, and materials science, with a strong publication record (h-index 36, i10-index 69). Dr Francesco Fanicchia, Research Area Lead: Material
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Computing Strategies: Develop algorithms that dynamically adjust computing resources to minimize energy usage without compromising performance. Thermal Management Techniques: Design systems that effectively
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with intelligent technologies. These agents will enable the creation of dynamic, evolving services across various sectors, including healthcare, urban intelligence, and education, fostering continuous
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the potential to transform clinical ophthalmology, offering improved diagnostic capabilities and deeper insights into the structural integrity of the retina. At a glance Application deadline03 Dec 2025 Award type
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FPGA/ASIC architectures that dynamically reconfigure based on AI workloads, optimizing performance, energy efficiency, and functionality. 3- Intelligent Interface Design: Create smart interfaces
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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or in an academic role. We will help you develop into a dynamic, confident and highly competent researcher with wider transferable skills (communication, project management and leadership) with
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
this, you will work with emerging AI techniques such as large language models, which can interpret technical documentation, and knowledge graphs, which help structure and connect engineering knowledge
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dynamically combine trusted and opportunistic signals. This project aims to develop versatile benchmarks for assured multi-domain PNT systems with advanced integrity frameworks, enabling rigorous evaluation and