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networks, online analysis with delay, and theory of distributed algorithms. Job description In our group, we try to apply and unite the approaches and techniques of theory and practice. Some members of our
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: Lightweight, real-time models using quantization, compression, and adaptive execution. Hardware–algorithm co-design: Cross-layer optimization aligning ML algorithms with embedded hardware. Distributed and
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processing and analysis. The LISA ground segment is a distributed facility (the LISA Distributed Data Processing Center), for which LISA member states contribute national centers with specific commitments and
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experience. Strong programming skills in Python and experience with modern ML stacks (PyTorch, HuggingFace, distributed training). Experience in LLMs, vision–language models, multimodal learning, or clinical
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Please submit the following documents with your nomination: Application form with details of the nominees and the nominator A title (max. 150 characters) and a brief summary of the achievement (max. 400 characters) A description of the achievement and the main results (max. 4’000...
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, algorithms, AI) in society. We are in particular looking for candidates who have interest and experience with STS and humanities pedagogy in the context of a technical university and in developing research and
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. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
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challenge. The ESA-funded LISA will rely on an extensive ground segment for data processing and analysis. The LISA ground segment is a distributed facility (the LISA Distributed Data Processing Center
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, you will design, prototype, and optimize advanced simulation algorithms—particularly in the domain of cloth and deformable materials and contribute to our next generation of rendering and learning-based
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100%, Zurich, fixed-term The Computational Design Lab is an interdisciplinary research group at ETH Zurich, led by Prof. Dr. Bernd Bickel . We develop novel algorithms and next-generation