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Mobile Edge Computing (MEC) has emerged as a promising computing paradigm to support emerging high-performance applications by deploying resources at the network edge. However, most existing MEC
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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Fixed-term: The funds for this post are available for 12 months in the first instance. The successful candidate will be based in the Department of Computer Science and Technology and will join the
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PhD Studentship available on the RAINZ CDT programme at The University of Manchester. Project Overview Abstract: Offshore wind and marine energy assets operate in harsh, inaccessible environments
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-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty
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maps of muscle activity in individuals with CNSLBP across multiple sessions. Specific Objectives - Identify optimal biofeedback practices (movement, dosage, duration) through a scoping review, patient
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Generative AI applications continue to expand, optimizing computational efficiency is becoming increasingly critical, particularly for AI in resource-constrained environments or at the edge. To address
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their wings for optimal performance. This PhD project will involve the design, fabrication, experimental validation, aeroelasticity modelling, and control of a small-scale wing prototype with a bio-inspired
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, & Buckingham, 2018). Research Aims and Objectives To determine which haptic cues are critical to optimize sensorimotor learning in surgical contexts. To investigate if proprioceptive cues delivered though a
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned