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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
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of analytical data to guide the generation of highly accurate 3D/2D molecular graphs or SMILES representations. Research Aims and Objectives This project aims to develop a robust, data-efficient deep learning
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Director, Marketing Technology, Data & Analytics Job No.: 689487 Location: 211 Wellington Road, Mulgrave Employment Type: Full-time Duration: 3 year fixed-term appointment Remuneration: A
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Overview This project proposes a novel quantum-enhanced learning analytics framework for higher education, focusing on early identification of at-risk students and optimisation of intervention
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feedback analytics! Project description: The ability to understand and act on feedback is one of the most powerful drivers of learning success. Despite decades of research affirming the value of feedback, it
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. If successful, you will join Dr. Roberto-Martinez Maldonado, Prof. Dragan Gasevic, and a strong team of academics, researchers and other students at the Centre of Learning Analytics at Monash University
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to real-life data. The goal is to generate new knowledge in the field of time series anomaly detection [1,2] through the invention of methods that effectively learn to generalise patterns of normal from
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to these protein structural characterisation scenarios. We will explore the applicability of advanced immersive visualisation and visual analytics using Augmented and Virtual Reality environments in visualising and
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Time series are an ever growing form of data, generated by numerous types of sensors and automated processes. However, machine learning and deep learning methods for analysing time series are much
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) to support surgeons, operating room technicians, and other professionals in and around operating room activities. Particular areas that may be explored are: Immersive OR analytics: using XR to analyse data