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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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contribute to the development and optimization of the training pipelines for these large-scale models, working at the intersection of cutting-edge research and high-performance computing to advance
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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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Addressing challenges related to high-dimensional parameter spaces and complex performance criteria commonly encountered in RBDO Developing robust computational workflows for optimizing designs while
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application of deep learning models. Experience with data pipelines following FAIR principles, with skills in compressed sensing, image noise reduction and big data management, cloud computing, high performance
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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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. Bonus points: Published peer-reviewed articles on project-related topics in reputable conferences or journals Experience with network simulators such as OMNeT++ or ns-3 Experience with High Performance
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design new methods for producing hydrogen from water that are not only compatible with sustainable heat sources (e.g., concentrated solar radiation) but also scalable by volume. The performance
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(using a predefined script) Transcribe interview sections of the session video recordings Review transcriptions performed via tools (e.g., OpenAI Whisper) to ensure accuracy Organize and clean the
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elasticity, dissipation, and slender geometry interact to influence crack initiation and growth. Using in-house codes and high-performance computing resources, the candidate will simulate fracture processes