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advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and
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Responsibilities: Conduct in-depth research in sublinear time and learning augmented algorithms. Design, develop, and implement novel algorithms and models. Publish research findings in leading international
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. In this role, you will be part of the research team, working to develop and evaluate privacy-preserved Generative AI algorithms for generating synthetic Personal Identity Information (PII). This aims
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and RISC-V architectures. Investigate various aspects of system design, including: Topology exploration, routing algorithms, protocol and flow control design Chiplet oriented and interposer design
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architectures. Investigate various aspects of system design, including: Topology exploration, routing algorithms, protocol and flow control design Chiplet oriented and interposer design strategies, SiP and
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. The roles of this position include: Development of algorithms to improve memory/sample/time efficiency of LLM training. Development of a workable prototype system with capabilities such as conversational
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Power Engineering • Relevant experience in power electronics, wide band gap semiconductor devices, multilevel inverters, soft-switching high-frequency power converters, drives, control algorithms
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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
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the following tasks: Development of new large language models for process modeling Development of new decision-making algorithms Provide regular project updates to principal investigator and funding agency Report
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Responsibilities Development of new machine learning modeling approaches Development of new advanced control and optimization algorithms Optimization of carbon capture process operation Provide regular project