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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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state-of-the-art research in search, optimization, and machine learning, focusing on addressing sustainability challenges in ICT, Construction, and Aerospace Oversee and mentor PhD students, ensuring
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systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, Machine Learning/AI, and IoT/5G on organisations from both
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knowledge and/or experience in several of the following topics: Optimisation algorithms Machine learning algorithms Swarm intelligence Algorithmics Parallel/Distributed computing Space systems engineering
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discipline The ideal candidate should have some knowledge and/or experience in several of the following topics: Optimisation algorithms Machine learning algorithms Algorithmics Smart buildings Internet
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, or any related engineering discipline The ideal candidate should have some knowledge and/or experience in several of the following topics: (Quantum) Optimisation algorithms (Quantum) Machine learning
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Research Group (SERVAL). The post will reinforce the strategic research roadmap of the group in engineering industrial machine learning systems, including the strategic partnerships with BGL BNP Paribas
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have some knowledge and/or experience in several of the following topics (ordered by importance): Wireless Communication Technologies Distributed and Embedded Systems Machine Learning Data Analytics and
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such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Our activities are experimentally driven and supported by the COMMLab