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. Profile Enrollment as a student at ETH Zurich or the University of Zurich in a relevant field, such as computer science, engineering, economics and mathematics Intermediate coding skills in R, Stata
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of generative AI algorithms for manufacturing Collaborate with an interdisciplinary team to transform research results into innovative solutions Develop high-quality code and build the basis for our industrial AI
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to solve selected problems. Code and test the proposed solution (PoC), communicate results to stakeholders, and provide final reports of the work done and related outcomes. Present data science results
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manufacturing processes (APIs, excipients, intermediates). Intermediate coding skills in Python. Interest in data-driven research with policy impact. Workplace Workplace We offer Your job with impact: Become part
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collected data Grade student answers and drawings by following a rubric Code session recordings with qualitative methods (using a predefined codebook) Collaborate with the researcher to refine the codebook as
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, perform the evaluations hand-in-hand with the domain experts, and eventually release open-source code and the associated documentation, write research papers or technical reports as appropriate, and
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economy, game studies, and/or code studies – would be welcomed. Also desirable would be experience (or interest) in local and global community building, academic and para-academic event organization, and/or
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, reproducible, and robust code in Python. The candidate will make use of and contribute to Anemoi , a toolbox for the use and implementation of AI tools for weather and climate applications, developed by
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experience in statistical analysis and implementing machine and deep learning models using Keras/TensorFlow and/or PyTorch. You have experience in collaborative coding, version control, and utilizing computer
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learning technologies. You will actively participate in code reviews and maintain comprehensive documentation to ensure code quality and reproducibility. Additionally, you may be expected to contribute