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will develop efficient quantum compiler methods to bridge the gap between quantum algorithms and their execution on quantum processors. The project will develop innovative quantum software components and
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evolutionary and behavioural game theory, multi-agent reinforcement learning, agent-based simulation, or experiments with people and AI systems. Some students may develop new theory or algorithms; others may use
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this project, the student will develop scalable artificial intelligence based methods such as deep neural networks or transformers to perform decoding in microsecond time scales while processing error
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analyse security vulnerabilities of shared quantum computers and develop software level solutions to ensure secure and reliable sharing of quantum compute resources. The Scalable Quantum Systems Lab
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Professor Muhammad Usman at the Faculty of Information Technology. The PhD project involves developing novel quantum artificial intelligence models which are intrinsically robust against adversarial attacks
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machine learning can be used to understand complex behaviours such as reliability, competence, transparency and trust. The position will design and develop innovative reasoning algorithms and knowledge
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, data processing pipelines, machine learning and generative AI applied to physical activity and sleep research. The position will contribute to the development of an AI-based behaviour change tool and
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estimation, confidence calibration, model routing, token selection, early exiting, adaptive visual processing, and efficient use of multiple foundation models. The work will combine algorithm development with
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. Selected methods can then be evaluated through real-world human-robot interaction using humanoid and mobile robotic platforms. Aim/outline The aim of this project is to develop multimodal world models
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This PhD project focuses on the design and evaluation of hybrid quantum–classical algorithms for large-scale data analytics and optimisation problems. The research will investigate how quantum