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paid. We expect the stipend to increase each year. Only Home students are eligible for funding. The start date is October 2026. The project aims to develop and optimize metal oxide aerogel materials
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Engineering, and Engineering Management. Students with interests in computational mechanics, optimization design, bioinspired design, sustainability management, machine learning, AI, uncertainty quantification
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environmental inputs, algae physiological parameters and microbial community eDNA data to develop predictive mechanistic models which can be utilised to develop an optimal cultivation strategy. The project is
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still hampered by: Ability to detect areas along the intertidal for optimal restoration3. Knowledge on how positive species interactions can be harnessed for rapid restoration4. Availability of devices
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safety questions: Determining optimal stored energy requirements for grid support, considering various timescales and power ratings. Reviewing and benchmarking storage technologies (lithium-ion batteries
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reducing waiting lists. This will be achieved through the following objectives: Acquire data and expert-based evidence and optimise data augmentation to ensure optimal hospital patient pathways through pre
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modelling framework multiple ML tasks as mentioned above, to ease the development burden from users. It will research unified and modular modelling strategies, capable of optimally fusing and aligning diverse
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, scalability, and adaptability to various applications such as autonomous systems, IoT devices, and wearable technologies. Research Focus Areas: 1- Neuromorphic and AI-Optimized Processors: Design AI-specific
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quantitative analysis skills and experience developing algorithms and/or conducting statistical analyses with biological datasets. Background and work knowledge in statistics, algorithms, optimization of novel
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sensing, and Electromyography (EMG) tools to understand user-device interaction and optimize real-world rehabilitation performance. The student will gain experience in AI, human biomechanics, smart textiles