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Website https://www.academictransfer.com/en/jobs/358703/phd-in-scalable-safe-ai-for-sem… Requirements Specific Requirements A master’s degree AI, Machine Learning, Data Science, Computer Science or a
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students, with direct engagement from Alzheimer Europe and its networks. This vacancy applies to key issue 4 on model-based health-economic evaluation. Your work will directly inform decision-makers about
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create transparency and coordination across many stakeholders. You will work in a strong multi-actor consortium with University of Twente (UT), Tilburg University, Saxion, regional networks (e.g
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Network Engineering. Our research involves complex information systems at large, with a focus on collaborative, data driven, computational and intelligent systems, all with a strong interactive component
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) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands. Additional information For more information about this vacancy, please
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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing
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, the sector would turn from a flexible net electricity producer into an inflexible net electricity consumer. The grid would lose flexible CHPs and need new, ad-hoc dispatchable capacity investments, as already
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department at TU Delft (Faculty of Civil Engineering and Geosciences) and work closely with Dr Louise Nuijens and an (inter)national network of collaborators. QUASI offers a unique opportunity to combine
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to work on the design and implementation of Oscillatory Neural Networks (ONNs) for physics-based computing applications. You as the successful candidate will be an integral part of the prestigious ERC
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to work on the design and implementation of Oscillatory Neural Networks (ONNs) for physics-based computing applications. You as the candidate will be an integral part of the prestigious NWO AiNED AI-on-ONN