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the development of meta-optimization techniques that can automatically search for the best algorithm-hardware pair for a given problem. While we have a history of success in optimizing digital
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efficiency. Your Job: Develop and apply meta-optimization that can automatically search for the best algorithm-hardware pair Tackle the challenge of computationally expensive meta-optimization procedures by
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algorithmic graph theory. The purpose of the role is to contribute to the project "Algorithmic meta-classifications for graph containment", working with Professor Matthew Johnson, Dr Barnaby Martin and
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contribute to the project “Algorithmic meta-classifications for graph containment”, working with Professor Matthew Johnson, Dr Barnaby Martin and Professor Daniel Paulusma from Durham University and Professor
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-based processing. This project will investigate event-driven learning approaches in the context of RL in an event-triggered fashion. Data efficiency will be improved by using meta-learning and pre
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contribute to the excellence of our academic community. KEY RESPONSIBILITIES: Creates and maintains a data dictionary and meta data. Supports efforts to ensure that data standards are developed and maintained
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structural and algorithmic graph theory. The purpose of the role is to contribute to the project “Algorithmic meta-classifications for graph containment”, working with Professor Matthew Johnson, Dr Barnaby
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basis for the first draft of written reports, and makes preliminary interpretations of the data. Assists with assembly, annotation, meta-genomic analysis, and genotyping using high-throughput sequencing
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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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