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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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., 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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Migration Analyst will join the Student Management System Transformation (SMST) Program, supporting the Data Migration Lead in delivering an end to end data migration solution to enable migration of data from
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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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of Machine Learning as the problem of approximating function f from the pair of measurements (x,y), and Optimization as the problem of finding the value of input x that maximizes the output y given
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and basic optimization techniques are essential. Students with backgrounds in Data Science, Applied Statistics, Machine Learning, Statistical Computing, Industrial Engineering, or Reliability
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Informal Settlements and their Environments (RISE) program. RISE is an internationally recognised interdisciplinary research program developing and evaluating nature-based and hybrid infrastructure
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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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In NeuroDistSys (NDS): Optimized Distributed Training and Inference on Large-Scale Distributed Systems, we aim to design and implement cutting-edge techniques to optimize the training and inference
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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