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research on biomedical sensor applications (biomedical sensor interfaces/ integrated mechanical strain sensors) analog/Mixed-Signal Integrated Circuit Design (CMOS, low-power, low-noise design) publication
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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, on fundamental aspects of atomic spectroscopy and quantum physics, and will finally learn how to develop a commercial quantum sensor. Your role: Basic research in the theory and experiments with hot
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institutions in five European countries and will be supported by a network of industrial and academic partners. About the position The objective of this project is the development and application of soft sensors
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable
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. Such multimodal energy sources will become increasingly vital over the next decades, not only as sources of renewable energy but also for high-tech applications, such as powering unattended wireless sensors
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tasks Further development of the sensor based on aerosol separation technique and field effect transistor Use aerosol measurement technique, electron-beam lithography and cleanroom equipmen Design of
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models. The scientist will conduct research using machine learning and classical parameterization methods on data from ocean gliders equipped with microstructure turbulence sensors, turbulence resolving
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning