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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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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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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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Applications are invited for a post-doctoral position to develop and apply advanced mass spectrometry and data analysis methods to investigate plant-microbe interactions in soil, within the research
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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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Job Description We are seeking a motivated postdoc to work on the design and development of i) a variety of sample preparation methods (mainly for blood samples) and ii) new fiber-based substrates
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of TSN-based in-vehicle networks. These networks carry mixed-criticality traffic and use TSN with multiple traffic shapers and redundant communication. You will investigate methods for runtime analysis
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be widely implemented in clinical programs. Job description The aim of this position is to develop new chemical methods for the modification of nucleic acids. This includes the design and synthesis
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conferences, and actively participate in knowledge exchange across disciplines. As a formal qualification, you must hold a PhD degree (or equivalent). Specifically, a PhD in Materials Science and Engineering