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Field
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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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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
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/ Institute: Occupational and Environmental Health Supervisor(s): A/Prof Jacques Oosthuizen Project level: Masters or PhD Funding: Applicant should apply for ECUHDR or RTP Scholarship Start date: Ongoing
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PETs: This aspect requires a significant math background as it involves exploiting various mathematical results to develop a concrete cryptographic algorithm. Although desired, background in advanced
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science conference [1]; one of our papers is recognised as Clarivate Web of Science HighCite (top 1% of papers for the field of research) [2]; three of our algorithms (TS-Chief, InceptionTime and Rocket
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: Dr Prashant Bharadwaj; Prof. Ralph Martins Project level: Masters, PhD Funding: Applicant should apply for ECUHDR or RTP Scholarship Start Date: Ongoing
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of fixing defects and the workload among software engineers. This project is aligned with Prof John Grundy (2019 ARC Laureate Fellow, Human-Centric Model-Driven Requirement Engineering) and Dr Chakkrit
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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
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This project will investigate and develop the ways in which AI algorithms and practices can be made transparent and explainable for use in law enforcement and judicial applications The Faculty