25 coding-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Itä-Suomen-yliopisto" positions in Switzerland
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of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency. There will be a significant computational component in deploying multi-GPU codes
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(FOPH) of Switzerland and the Centers for Disease Control and Prevention (CDC) of the United States. We value good software engineering practices. We perform code reviews, have automated testing pipelines
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of research excellence through peer-reviewed publications Evidence of comittment to reproducibity in bioinformatics through the use of Code Repositories Evidence of ability and experience in handling
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programming skills (ideally Python) and commitment to clear, reproducible, well-structured code Working knowledge of SQL and/or NoSQL databases (and motivation to deepen your expertise) Strong analytical
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mechatronics systems is a plus Excellent coding skills in python, ROS, and RL&IL pipeline experience on simulator and training libraries. Knowledge on C++ is a plus Experience with Physics simulators such as
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) Unsupervised machine learning and deep learning methods Analysis, visualization, and interpretation of learned design spaces Contributing to research outputs (prototypes, publications, open-source code) Profile
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will be responsible for a range of research tasks, including literature search, data curation, coding for systematic literature reviews, data analysis, and miscellaneous administrative activities (e.g
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plus. You enjoy working with complex, multimodal datasets and developing robust algorithms for continuous monitoring and predictive modelling. You are comfortable combining coding, data analysis, and
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development, validation, and safe integration of locally hosted LLMs for automated coding of pediatric diagnoses from electronic health records (EHRs), with the goal of enhancing research capabilities and
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community Contribute to R data publication packages washr and fairenough Profile You care about data and code being concise and easily reusable You know how to use standard data science tools (Git, GitHub, R