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Field
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) environment. This role focuses on developing, implementing, and evaluating ML algorithms and systems, supporting the advancement of capabilities from early-stage concepts to prototype demonstrations
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energy resources. The expected outcomes include technical advancement of distributed algorithms for managing energy resources at customer premises. The benefits include more resilient, secure, private, and
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models; Learning with limited supervision; Generalisation beyond pretraining distributions The precise research agenda will be developed together with the successful candidate and may evolve throughout
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. Develop and evaluate algorithmic solutions (e.g., machine learning, optimisation, statistical modelling, distributed systems), depending on project direction. Implement research prototypes and run
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-mentor relationship Prompt disclosure to the mentor regarding the possession and desire to distribute materials, reagents, software, and copyrightable and potentially patentable discoveries derived from
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National Lab, University of Tokyo etc.), the PhD candidate is expected to research on some of the following themes: New algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient
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. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process management. Addressing data sparsity and data quality issues in industrial process data
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stressed electrical grid. Modern integrated building automation and control systems create an environment rich with sensor data and distributed control intelligence that can be applied to solve
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across tasks; Structured and compositional learning; Vision-language and multimodal models; Learning with limited supervision; Generalisation beyond pretraining distributions The precise research agenda
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- and long-term forecasting. Design hybrid AI architectures and optimize them using advanced techniques such as attention mechanisms and evolutionary algorithms. Implement probabilistic and explainable AI