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microbial communities. In this role, you will develop hybrid species distribution models that combine climate and landscape data to predict how microbial taxa niches shift under changing land use and
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, machine learning, and information-theoretic approaches to achieve robust, non-intrusive security for the ever-expanding IoT landscape. Feature Engineering for Encrypted Traffic: It is crucial to identify
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their primary function is to generate renewable energy (at the cost of covering large areas with solar panels), there is growing recognition that solar parks also offer a unique opportunity to restore and enhance
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power systems. You will join a large group of postgraduate students in the Faculty of Engineering, working on many aspects of solar energy and zero carbon technologies. The team of potential PhD
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optimization techniques, coding new algorithms, creating new mathematical theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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large upfront costs, but with ongoing cost savings once investments have been made. The liability to make these investments is tied to ownership of expensive assets (residential property). Other issues
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. Background and aims: As we move towards the future of large communication networks and remote sensing, applications such as 6G communications will require higher data rates, wider bandwidth, and stronger
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The project: The deployment of generative AI—particularly Large Language Models (LLMs) based on transformer architectures—in industrial settings poses several critical challenges. Ensuring reliable
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interaction and origins of mechanical failure under pressure. They should have expertise in microelectrode arrays and multilevel high-density routing for large-area sensor systems. Experience in multicomponent