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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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(technical personnel and toilet users), implementing AI-driven data analytics for system optimization, and creating predictive models for environmental and social impact assessment. Students will work with
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through long-term impact assessment and optimization. The goal is to develop a framework to estimate carbon emissions across AI's development, operation, and use. This framework enables stakeholders
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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems
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optimization of GRAS microorganisms at 1-10 L fermentation scale. ii. Design and optimise downstream bioprocessing for protein recovery. iii. Perform characterization of protein isolates for incorporation
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breakwaters under varied sea conditions. 2. CFD and Finite Element Modeling Perform Computational Fluid Dynamics (CFD) simulations to optimize the design and performance of floating breakwaters. Develop finite
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opportunities across online and offline channels. Align closely with Marketing, Admissions, and Finance to optimize the full applicant journey. Stay current on market trends, competitor activity, and regional
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challenge, making energy-efficient computing a critical research priority. This project addresses this challenge through a novel co-design approach that simultaneously optimizes both hardware and software
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meaning from data. Designing machine learning algorithms and undertaking testing for: predictive maintenance. operational performance and cost optimization. failure prevention and timely reparation of Aids
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Accountabilities Design and implement software pipelines to deploy ML models on edge devices with real-time inference capabilities. Optimize machine learning models (e.g., quantization, pruning) for edge hardware