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dynamic mobility of UAVs, limited onboard energy, and the stochastic nature of 3D wireless channels. Therefore, this project aims to develop novel reliable resource orchestration solutions for AEC by
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, particularly the strategy-as-practice and process-based research traditions, while engaging closely with AI governance and algorithmic organising. It examines how generative AI is transforming core managerial
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management systems (BMS). Ability to develop and implement algorithms for modelling, estimation, or control applications. Strong analytical thinking, problem-solving ability, and capability to conduct
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, which frequently produces designs that are suboptimal when subjected to real-world dynamic environments. Although a handful of advanced, high-fidelity solvers have been developed to tackle this issue
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-time robotic control and AI pipelines. You will design and tune control algorithms to ensure precise, stable, and safe remote robotic operation under communication constraints. You will develop and
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both individual decision-making and multi-agent cooperation. The student will investigate two complementary directions: ToM-Enhanced Decision-Making for Autonomous Agents: Develop decision-making
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. The studentship will start on 1st October 2026. Project Description Project Aim To develop and validate an AI co-pilot software system integrating multi-modal radiomics data to enhance cancer detection speed and
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms
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size, weight, power and cost constraints, accurate relative positioning and motion knowledge across platforms, coordinated data acquisition strategies, and the development of SAR image formation
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PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis for synthesis. A doctoral position is