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at the Edge (CAITE) whose aim is twofold. First, it aims to bring AI at the edge, by providing scalability and resource efficiency through the development of cooperative, distributed AI algorithms, optimising
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geometry and variable stiffness composites Implement bespoke optimization algorithms combining shell geometry and fibre distribution accounting for nonlinearity and uncertainty Conduct optimization studies
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of the physics applications and algorithms; Create detailed documentation for the developed applications, including user guides and technical specifications; Train users and operators on how to use the new tools
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: 6586266 Assistant Professor of Teaching (Lecturer), Algorithms & Computer Security Position Information Position Title: Assistant Professor of Teaching (Lecturer), Algorithms & Computer Security Department
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management, cache optimization, and vectorization techniques. Strong understanding of algorithms and data structures, especially those suitable for parallel processing and distributed computing. Understanding
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architectures such as convolutional neural networks, transformers, and diffusion models. Proven experience building AI solutions using classical ML algorithms such as decision trees, gradient boosting machines
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healthcare application needs to analyze sensitive patient data across distributed nodes. Researchers and students can explore privacy-preserving algorithms and technologies like federated learning and zero
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short- and long-term demand prediction, renewable generation forecasting (solar, wind, hydro) under uncertainty, spatiotemporal modeling for distributed energy systems, energy markets, transfer learning
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stiffness composites Implement bespoke optimization algorithms combining shell geometry and fibre distribution accounting for nonlinearity and uncertainty Conduct optimization studies and oversee
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for in-space manufacturing and testing. Develop AI algorithms for navigation, guidance, and control. Lead collaborative research proposals for NSF, DoD, DOE, NASA. Mentor students and build