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Field
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to deliver on an industry innovation research project where you will be part of the research team to design, develop and evaluate schemes for quantum key distribution networks and systems. Key
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in a connected context, investigating how accelerators can be designed, integrated, and scaled for emerging large scale and networked applications. We are interested in new ways of virtualising
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, recurrent memory, Bayesian modelling, uncertainty quantification and machine learning systems. Emphasis will be on methods that design and implement new architectures for (auto-regressive) sequence modelling
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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
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anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated Applied AI for Healthcare and
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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technical ideas, and engaging with GenAI industry and open-source communities. Job Responsibilities: Conduct the research in AI/ML domains, especially in probabilistic ML and scalable sequence network
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across academia, government, industry, clinical partners and investors. International experience and strong networks in Singapore’s innovation ecosystem and global industry are advantageous. We regret
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seek to create a friendly and inclusive culture. Diversity is positively encouraged, through our EDI Committee, working groups and networks, for example eng.ox.ac.uk/women-in-engineering, as well as a
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subscribe to Equality Charter Marks such as the Diversity Charter Mark NI and Athena Swan and have established staff networks such as iRise (Black, Asian, Minority Ethnic and International Staff Network) and