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This research aims to design a sustainable framework for optimizing distributed computing systems to enhance performance while minimizing energy consumption. Existing scheduling algorithms often
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and deeply engaged with staff and students, supporting wellbeing and belonging while creating the conditions for bold ideas, strong performance and shared success. They will bring energy, optimism and
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., Boruta) and statistical imputation methods (e.g., KNN/Iterative Imputer) are applied to optimize variable efficiency and handle missing data. Ensemble algorithms—specifically Gradient Boosting, LightGBM
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Familiarity with Python and Callista data is desirable About Monash University At Monash , work feels different. There’s a sense of belonging, from contributing to something ground breaking – a place where
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and dedicated to making a meaningful impact in patient care, we invite you to apply now. Monash University seeks applicants for this opportunity who are able to demonstrate present valid and current
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investigate the scientific, technical and institutional innovations required to enable large-scale impact. Successful candidates will join the Transforming Cities Hub at Monash University and work within an
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In EdgeVLMOpt (EVO): Optimizing Vision-Language Models for Resource-Constrained Edge Devices, we aim to develop efficient and scalable techniques to enable the deployment of advanced vision-language
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research often overlooks the complexities of mixed-vehicle environments, and the development of optimal deployment, routing, and charging strategies. This project aims to address these gaps by optimising
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compressed into lightweight student models using knowledge distillation, enabling efficient real-time inference on mobile devices. The distilled models will be deployed and optimized on mobile platforms, with
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determining the appropriate design pattern for a specific scenario, identifying relevant quality attributes for a particular design choice, and recognizing the optimal timing for implementing a refactoring