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testing data Development of machine learning models for battery health assessment and remaining useful life prediction Job Requirements: PhD degree in Electrical Engineering or related subjects. Expert
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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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on a project on the topic of onshore microgrid energy management system development. Key Responsibilities The roles of this position include: Development of the onshore microgrid model including static
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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
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-tier conferences. Demonstrated expertise in large language models (LLMs) and their application to AI security, autonomous system reasoning, software vulnerability detection, and robust agentic AI design
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students, visiting scholars and researchers Conduct modelling, simulation and experimental research on mechanics of soft robotics Explore applications in the fields of transportation and search and rescue
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model for innovative medical education and a centre for transformative research. The School’s primary clinical partner is the National Healthcare Group, a leader in public healthcare recognised
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model for innovative medical education and a centre for transformative research. The School’s primary clinical partner is the National Healthcare Group, a leader in public healthcare recognised
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role will focus on nanophotonics theory and modelling. Key Responsibilities: The Research Fellow will work on a project to conduct the research on development of nanophotonic structures for quantum and
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems