41 distributed-algorithm-"Meta"-"Meta" Fellowship positions at Nanyang Technological University in Singapore
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, or a related field of thermal nanophotonics. Excellent publication records will be preferred. Experience in matlab, MEEP, RCWA and other softwares and algorithms. Excellent publication track records
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, quantum machine learning, quantum algorithms from well-established universities/institutes. The candidates must be highly motivated in multidisciplinary research. He/she must have proven experience in
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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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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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Digital Twin for electric vehicle applications . The role will focus on utilizing novel machine learning models, large language models, and data science algorithms to develop battery models, optimization
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maintenance of both mammalian and microbial cells Devise micro-bioreactor for co-culture of mammalian and microbial cells Program control algorithm for optical sensors Design, fabricate, and optimize
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Responsibilities: Development of stochastic and analytical methods for nonlinear partial differential equations Implementation of relevant numerical experiments using deep learning algorithms Job Requirements: PhD
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: Identify market-readiness of IDMxS inventions Coordinate the preparation of marketing materials for distribution and dissemination Research local and global industry trends and needs Develop Communications
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of innovative solutions in energy efficiency and distributed energy resources. ERI@N distinguishes itself through research excellence directed towards outcomes of industry relevance, with focus on systems-level
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of innovative solutions in energy efficiency and distributed energy resources. ERI@N distinguishes itself through research excellence directed towards outcomes of industry relevance, with focus on systems-level