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and reproducible research, e.g., in the development of codes and algorithms. We will focus on devising computational solutions that can immediately be of use in other applications contexts as well
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
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and polyploid crop species and benchmark them against other methods such as graph-based methods. This project will combine algorithm development and computational programming with large population
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the different types of systems and develop a core graph data system that can serve as a common building block. This way, redundancies in keeping multiple cop-ies of graph data in different systems could be