74 computer-programmer-"https:"-"Prof" "https:" "https:" "https:" "https:" "IFM" "IFM" "IFM" uni jobs at ETH Zurich
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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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operations that are yet to be fully understood. In this context, it is evident that the operation, control, and planning of power systems will soon be pushed to their limits. Therefore, new computational
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convective heat transfer with the surrounding air. Within our research group at ETH Zurich, we are developing computational workflows for predicting temperature fields in machine tools using computational
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scientometrics Profile Background Master’s degree ideally in information science, library science, data science, computer science, or a comparable field; a PhD is an advantage Regardless of academic background
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using emerging non-invasive exhalomics (exhaled breath metabolomics) approach. The research program integrates animal experiments with advanced analytical techniques and bioinformatics to enhance
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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
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evaluate prototypes together with industrial partners Profile Required experience CH/EU/EFTA citizenship or valid Swiss work permit PhD in Engineering, Computer Science, Robotics, or related field Strong
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is to build efficient and robust computational tools for analyzing complex engineering systems. Applications include structural dynamics and other dynamical systems relevant to real-world engineering
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. Neuromorphic computing and ML deployment on digital and neuromorphic processors TinyML and EdgeAI and ultra-low-power inference for resource-constrained systems Techniques such as quantization, pruning