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expand both actuation and sensing capacities with light. The project will create an artificial sensorimotor network with densely distributed actuators and sensors similar to the biological neuromasculature
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maintained and distributed by C2SM is EXTPAR (External Parameters for Numerical Weather Prediction and Climate Application), which prepares external parameter data for use with the ICON (ICOsahedral
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Design and microfabrication of soft optical fibers densely distributed with actuators Develop actuator multiplexing methods for individual addressability Demonstration for advanced robotic functions as
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and professionals across emerging areas like machine learning, cyber security, climate risk, distributed ledger technology, and quantum computing and translates that expertise into integrative research
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Experience with high-performance or distributed computing environments Good understanding of meteorological processes and numerical weather prediction Interest in DevOps practices and sustainable software
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datasets The position is limited to two years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep
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years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep-learning models on distributed systems
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the BedrettoLab, evaluation of seismic sensors (e.g., different types of seismometers as well as distributed acoustic sensing technology) Design and Installation of a Multi-Sensor Monitoring Network in the Hondrich
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The power system is changing, largely driven by the energy transition and climate change. The large shares of renewables, both centralized and distributed, are posing new challenges to system
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, particularly with respect to fouling and moisture distribution. Understanding these parameters is essential for predicting long-term track performance and planning maintenance interventions. Project background