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different levels of the environmental stress index. In addition, it will employ cross-sectional and longitudinal econometrics and machine learning techniques to determine the relationship between
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mindset and aptitude for interdisciplinary research - machine learning skills are mandatory - experience with training and/or designing neural networks for language processing tasks is strongly desired
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proposes a solution to the coordination and management of distributed DTs. A machine learning enhanced digital twin technology is proposed to facilitate the coordination in and across operational silos
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vis-à-vis changing environmental/climatic conditions; (2) analysis of viewsheds and the phenomenology of land-, sea- and icescapes; and/or (3) remote-sensing-based machine-learning applications
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representations in the form of ontologies and knowledge graphs, while neuro-symbolic learning can be a powerful mechanism to drive knowledge-infused machine learning. The incorporation of domain knowledge is hardly
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addition, it will employ cross-sectional and longitudinal econometrics and machine learning techniques to determine the relationship between environmental stressors and population health. This project is
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Treatment: Distributed Digital Twin for Clean Wate) funded by the NWO Merian Fund proposes a solution to the coordination and management of distributed DTs. A machine learning enhanced digital twin technology
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candidate should have: a Master's degree (or equivalent) in Computer Science or a related field good programming skills and experience with distributed computing frameworks knowledge of machine learning and
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within the extensive project partnership, able to perform within project timescales competences in data analytics, machine learning / AI (for example in Python and relevant machine learning libraries), as
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can be a powerful mechanism to drive knowledge-infused machine learning. The incorporation of domain knowledge is hardly achievable without the collaboration of multi-stakeholder teams, and therefore a