123 parallel-and-distributed-computing-"Meta"-"Meta" positions at ETH Zurich in Switzerland
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ETH Zurich, Office for Faculty Affairs Position ID: 2253 -PROF2 [#26726] Position Title: Position Type: Tenured/Tenure-track faculty Position Location: Zurich, Zurich 8000, Switzerland [map ] Subject Area: Mathematics - Probability Theory Appl Deadline: 2025/09/15 11:59PM (posted 2025/07/04,...
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on Human Factors in Computing Systems, Lancet Digital Health, npj Digital Medicine). You will also be involved in teaching activities.
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on the following activities: Writing scripts for the automated collection and cleaning of data, and organizing it into a database Computing simple statistics Preparing graphical representations Testing surveys
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afterwards. You will work closely with our research team to implement a new version of our RAG-based chatbot. Profile The ideal candidate will be a computer or data science student, or a student with extensive
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the ETH Zurich employee benefits program chevron_right Working, teaching and research at ETH Zurich We value diversity In line with our values , ETH Zurich encourages an inclusive culture. We promote
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engineering, computer science, data science, or a closely related discipline Have an excellent academic record Have strong analytical skills Be passionate about sustainability, energy, and public policy Be
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, microfluidics, patient samples, microscopy, in vivo models, genome engineering, single-cell analysis, and computational and molecular biology to elucidate the inner workings cancer cells and adult stem cells. Job
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Emulators of Stochastic computational models"), funded by the Swiss National Science Foundation (SNSF). The project aims to significantly advance the state-of-the-art in uncertainty quantification (UQ) by
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Required experience: CH/EU/EFTA citizenship or a valid work permit for Switzerland A Master’s degree (ETH, university) in engineering, computer science, or a related field Strong academic performance meeting
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, electrical engineering and computer science to design highly efficient and sensitive imaging and inference approaches to help guide diagnosis and treatment in cardiovascular patients. Project background Our