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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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mutual learning. Access to high-performance computing. Terms of employment: The desired starting date is September 1st, 2025. We are looking for a postdoctoral researcher/senior scientist for an initial
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, continuous integration). Adapt software for parallel computing, optimization workflows, and high-performance computing environments. Contribute to software deployment, testing, and benchmarking across multiple
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, optimized for high-performance computing (HPC) environments. Classifying ice crystal habits using Convolutional Neural Networks (CNNs). Providing intuitive graphical interfaces for user interaction and data
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for their contributions (ETH medal, CHI Best Paper Award), we will first conduct a real-world study in which subjects are put under the influence of cannabis while performing a pre-driving test and a driving task on a test
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experience with genomic data analysis on a high-performance computing cluster, along with strong communication skills, is desirable. Profile Research interest in statistical genomics, computational biology
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fully funded, full-time PhD position focused on the development of novel, energy-dense materials for asynchronous storage and dispatch of high-quality heat. The position is expected to start on 1 August
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the SVT course programme Contributing to the operation of the group and the Institute Profile You ideally have a Master’s degree in computer science, artificial intelligence, transportation engineering, or
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. Experience with running numerical models is an advantage. Experience in working in a Linux/Unix high-performance computing environment is a plus. Good communication skills in English (written and spoken
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Addressing challenges related to high-dimensional parameter spaces and complex performance criteria commonly encountered in RBDO Developing robust computational workflows for optimizing designs while