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stakeholders in the Dutch battery ecosystem to develop and demonstrate the next-generation algorithms and models for the future Battery Management System. The PhD student will work on topics related to: Develop
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? Join us to develop deep learning techniques for fusing acoustic sensor data with other vehicle sensors for robust multi-modal environment perception. Help shape the future of autonomous driving! Job
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, enabling energy-efficient, quiet, and long-duration monitoring of ecosystems. The research will integrate novel lightweight perception modalities for robust perching in the wild, agile control algorithms
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Vacancies 2x PhD position on the design and fabrication of MEMS flow sensors and microphones Key takeaways In the Integrated Devices and Systems (IDS) group, we will start two new research projects
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to develop a new biosensing technology, for the continuous real-time monitoring of proteins and nucleic acids? Join us in tackling this challenge! Information Continuous glucose sensors are used by patients
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the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes
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characterization to device integration. You will fabricate and test cutting-edge device architectures, including 2D Complementary FETs (CFETs), memory devices, and sensors, pushing the boundaries of what's possible
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: collaborate with the group of Dr Arnold Boersma at the Bijvoet Centre to develop novel sensors for measuring macromolecular crowding in C. elegans; use advanced fluorescence microscopy techniques, complemented
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sensors for measuring macromolecular crowding in C. elegans; use advanced fluorescence microscopy techniques, complemented by molecular biology and biochemical methods; be an active member of an open and
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning