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background is required with documented expertise in the following research areas: Expert understanding and ample track record of Mass Spectrometry-based proteomics Strong track record in both method
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in time-predictable computer architecture. Designing a network-on-chip for real-time automotive systems Verify the design with modern verification methods, such as function verification and formal
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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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to monitoring key performance indicators (KPIs) of the reManuFactory project. Engage companies through Co-Labs, Pilots, and Drop-In formats, to be demonstrators of the methods developed in the wider reManuFactory
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performance computing numerical methods in our state-of-the-art open source micromagnetic model, MagTense. MagTense is based on a core implemented in the Fortran programming language, and it relies
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Associate Professor Thor Grünbaum. The larger project develops and tests a new theory of basic cognitive selection mechanisms by combining methods and perspectives from experimental psychology, cognitive
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positive and supportive team environment. You prefer to stay organized and take pride in completing your tasks in a thoughtful and structured way. As a formal qualification, you must hold a PhD degree (or
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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(XAI) methods to improve the understanding of key drivers controlling peatland conditions and ecosystem functioning. The research project will primarily focus on implementing and merging analyses