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(University of Adelaide). Project 1.4. Quantum biosensor development (University of Adelaide). Project 1.5. Quantum chemical sensor development (University of Adelaide). Project 2.1. Superconducting quantum
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Optimisation methods, such as mixed integer linear programming, have been very successful at decision-making for more than 50 years. Optimisation algorithms support basically every industry behind
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; wireless sensor networks; green communications; next generation wireless broadband networks; signal processing and acoustics; digital image processing; structural health monitoring and underwater
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defence algorithms are remain a challenge in the research community. Furthermore, most existing AML algorithms can only apply to Euclidean space. How to extend existing AML algorithms to non-Euclidean and
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This PhD project aims to mitigate the data scarcity of new NLP and Multimodal applications by developing novel active learning algorithms. In this project, the student will leverage large foundation
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Engineer, assist in the development and implementation of novel fermentation strategies Implement a structured maintenance schedule for bioreactors and probes/sensors Under the supervision of our senior
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Seizure prediction algorithms will be developed using the one-of-a-kind ultra-long-term human intracranial EEG dataset obtained from the Neurovista Corporation clinical trial of their Seizure
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quantum engineering, quantum computing, and/or quantum information sciences A deep understanding of quantum algorithms and/or experience with quantum hardware will also be beneficial. A passion for
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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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approximation algorithms for deriving dual bound within a branch-and-bound algorithms. Other directions could use Machine Learning or new decompositions. This subject is generally quite open so it is important to