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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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NLP is highly welcome. For computational candidates: experience in large-scale ML training (multi-node setups, distributed training) is a strong advantage. Good computational engineering practices
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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
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CS Practical knowledge of RL and IL (e.g., behavioral cloning, inverse RL, dealing with distribution shifts) with applications in (industrial) robotics Hands-on experience with robotics or other
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component of solid-state transformers (SSTs). Such SSTs are required, for example, in future AI data centres, where power consumption per computer rack increases to levels of several hundred kilowatts or even