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Field
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selection of sensors and their respective lighting to be adopted.; 3. Study and development of algorithms for detecting inconsistencies.; 4. Study and implementation of operator interfaces.; 5. Assembly and
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efficiency through parallelism (time-, frequency-, and mode-multiplexing), with a specific focus on photonic reservoir computing Relate parallelism to applications, e.g., algorithmic parallelism, multi-tasking
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Relate parallelism to applications, e.g., algorithmic parallelism, multi-tasking, etc. Address nonlinear equalization in optical signal transmission and provide a comparison with neuromorphic electronics
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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-house database of experimental real-world data enabling large-scale validation of developed algorithms. Wind turbine drivetrains are critical components, and their failures can lead to significant
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. Knowledge Graphs based on engine propeller combinator diagrams of the same vessels. Machine learning algorithms for data clustering and regressions of ship performance and navigation data sets as a part of
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this PhD project, you will investigate the co-design between event-based learning algorithms and neuronal hardware units with multi-scale time constants. The algorithmic methodology will exploit recent
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/ is a future-oriented research group with the main strengths and focus topics: Self-driving vehicles, driving algorithms and cyber-physical system Sensor fusion, perception and big data Cybersecurity
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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of algorithms and digital neuromorphic hardware is an additional avenue for enhancing the efficiency of the methods. In this context the research will explore digital, event-based implementations