71 programming-"https:"-"FEMTO-ST"-"UCL" "https:" "https:" "https:" "https:" "IMEC" positions at ETH Zurich
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, we plan to study atmosphere/ocean interactions at multiple spatial resolutions, and investigate consequences for tropical cyclones, clouds and precipitation and how they depend on spatial resolution
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-reviewed articles Profile Required qualifications and skills: Master’s degree in meteorology, climate science, physics or equivalent with demonstrated expertise or qualifications Programming experience in
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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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Switzerland for more than 12 months in the 36 months immediately before their recruitment date. The student will be enrolled in the structured PhD programme of the Department of Mechanical and Process
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their recruitment date. The student will be enrolled in the structured PhD programme of the Department of Mechanical and Process Engineering or the Department of Health Sciences and Technology
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disciplines) Good programming skills (Python, MATLAB) Interest in machine learning (ML) and structural mechanics Motivation to work on interdisciplinary research problems Independent and structured working
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demo and pilot a success that builds confidence, trust, and long-term customer relationships. Your Contribution Plan, organize, and execute customer demonstrations and pilots, including equipment
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to the research group’s teaching responsibilities, which involves teaching courses for ETH’s teacher training program in German. Further, the candidate should be prepared to co-mentor PhD students and to mentor
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Zurich or another Swiss university Proven experience in software development Strong Python programming skills, with demonstrable experience in several of the following areas: GUI development CUDA for GPU
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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