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compromise grid reliability and energy security. We proceed in two steps. First, we use AI-based document analysis and engineering data to construct detailed bills of materials (BOMs) for selected critical
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clinical data usability. This position is embedded in a close collaboration between ETH Zurich’s Medical AI Lab and the University Children’s Hospital Basel (UKBB), enabling direct interaction with
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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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page max) Statement of future research interests (2 page max) Contact information for 3 references Please note that we exclusively accept applications submitted through our online application portal
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portal. Applications via email or postal services will not be considered. For more information, please visit our website . Questions regarding the position should be directed to Amanda Gogates
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Transcript of records Etc. Further information about AMLZ can be found on our website . Questions regarding the position should be directed to Sebastian Lang, selang@ethz.ch (no applications). Please note that
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research achievements and research interests Detailed CV Transcripts of all degrees (English) Names and contact information of at least 2 references Representative published research work (Papers or thesis
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to efficiently train on the large, densely-connected and graph-structured data encountered in our systems of interest. Your contributions would be across the spectrum from methodological development
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. ENDOTRAIN will train a new generation of interdisciplinary experts who merge clinical endocrinology, artificial intelligence, data science, engineering, ethics and law into an integrated field of digital
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future . Curious? So are we. We look forward to receiving your online application with the following documents: Application Letter Updated CV Names of two references Further information about the