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Research Assistant / HiWi (8 hours/week) - Climate Opinion Data Analysis with R - Faculty of Busines
Research Assistant / HiWi (8 hours/week) - Climate Opinion Data Analysis with R - Faculty of Busines The research group of International Political Economy and Energy Policy, led by Prof. Aya Kachi
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; maintain our custom-made R package and provide support to the research teams; support panel management tasks such as data cleaning and data storage. Profile Requirement excellent oral and written
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materials, federal administration and nature conservation representatives, and members of the Swiss Engineering Association (SIA). Urech, P. R., Dissegna, M. A., Girot, C., & Grêt-Regamey, A. (2020). Point
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with the CEO, the R&D team, and external consultants to time regulatory steps smartly Strategic & Commercial Readiness Identify potential commercial or strategic partners for sub-products and the
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. Candidates should be able to independently conduct statistical analyses in R, be able and willing to conduct fieldwork in the Swiss Alps, and have knowledge in plant species identification. Prior experience
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(target gene selection, power analyses, guide-library design, readout selection). Build, maintain, and document reproducible analysis pipelines (Python/R; Snakemake/Nextflow preferred) for novel
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frameworks (e.g., Python, R, TensorFlow, PyTorch). Experience with biological or animal science data (e.g., omics, time-series production data) is highly desirable; familiarity with ruminant nutrition
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irradiation. M. Oppermann, B. Bauer, T. Rossi, F. Zinna, J. Helbing, J. Lacour, and M. Chergui, Optica 6, 1, 56-60 (2019). https://doi.org/10.1364/OPTICA.6.000056 M. Oppermann, J. Spekowius, B. Bauer, R
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large-scale, consortium-based projects is a plus. Proven skills in data analysis and programming (MATLAB, R, Python, etc.); experience handling large datasets is a plus. Additionally, skills using Linux
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, deep learning), and AI frameworks (e.g., Python, R, TensorFlow, PyTorch). Experience with biological or animal science data (e.g., omics, time-series production data) is highly desirable; familiarity