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Field
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engineering teams to implement and test models in production environments What We’re Looking For We’re looking for research scientists with a proven track record of applying deep learning to solve complex, high
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Australian Research Council (ARC) Funded PhD Opportunity at Faculty of Engineering: High-Speed Rail and Sustainable City Sizes in Australia Location: Clayton campus Department/Unit: Monash Institute
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comprehensive analysis of the extensive Pulse dataset, uncovering latent patterns and taxonomies that define building leakage characteristics. Surrogate Model Development: You will develop data-driven surrogate
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invites applications from candidates with a robust foundation in data science, modelling, and/or engineering, and a keen interest in deploying data analysis and artificial intelligence (AI) to solve real
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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interdisciplinary training in AI, modelling, and data analytics Contribute to real-world engineering applications Be part of the dynamic research community at the Zienkiewicz Institute for Modelling, Data and AI
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-class honours degree or equivalent) in materials science, manufacturing, mechanical engineering, metallurgy, physics, chemistry, or related fields. Ideal candidates will be self-driven, eager to learn CFD
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inflammation. The ideal candidate is a passionate and driven scientist who thrives in a collaborative, fast-paced academic environment. A strong research background, outstanding writing skills, and the ability
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university research into commercial outcomes. Under this program, PhD students will gain unique skills to focus on impact-driven research. This Project aims to develop a predictive machine learning model
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, convective exchange with the environment, biochemical quality attribute evolution within the products, and thermally-driven damage (chilling-freezing injury). Use these models to build physics-based digital