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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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Contextual Data Analytics (ICDA) as a method to address contextual analysis challenges by bringing rich contextual information to the analyst’s workspace. Despite the technological capability to support ICDA
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Full-time, continuing position within the Advanced Analytics, Planning and Enterprise Data team Lead the University's research analytics capability, driving research excellence, impact and
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market research and analytics to complex challenges, translate data into meaningful recommendations, and build a strong culture of evidence-based decision-making. You will play a key leadership role
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across the University. You’ll play a key role in identifying opportunities to streamline, digitise and automate financial processes, while delivering insightful analytics through data extraction, reporting
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senior leaders, you will lead the University's planning, performance, reporting, analytics and business intelligence functions. You will provide high-quality information, analysis and advice
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- $115,686 + 17% superannuation About the opportunity The Faculty of Engineering is currently seeking a Data Insights Analyst to extract and visualise data to derive insightful analytics, employing
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Adobe Experience Manager (AEM) and Adobe Journey Optimizer (AJO). Working across Digital Experience, Sales, Analytics, Marketing and IT, you will use customer insights, behavioural data and journey
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, including the analysis and interpretation of complex data, development of robust reporting and analytical solutions, and delivery of actionable insights to support operational, strategic and evidence-based