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WSL Institute for Snow and Avalanche Research | Davos Dorf, Kanton Graub nden | Switzerland | 3 months ago
global change. The group has strong expertise in water resources, extreme value analyses, statistical modelling, and climate impact assessments and is looking, from summer 2024, for a PhD student in
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of econometrics and first experience with statistical programs such as STATA, R, or Python and a high willingness to attain excellent econometrics skills including on machine learning You are fluent in
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science, statistics, applied mathematics, or related fields Proficiency in developing and deploying machine learning models (e.g., using Python, R) Experience in data wrangling and feature engineering
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bioinformatics, statistics, and programming The candidate must have a strong motivation and interest in collaboration with multi-disciplinary scientists. They must be able to communicate effectively in a highly
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or equivalent qualification - Interest in mechanistic and statistical modelling of the soil-plant system - Good knowledge of R or Python - Experience in analysing spatial data with ArcGIS or QGIS is an advantage
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mathematics Mathematics » Statistics Technology » Biotechnology Researcher Profile First Stage Researcher (R1) Country Switzerland Application Deadline 1 May 2024 - 23:59 (Europe/Zurich) Type of Contract
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science, statistics, applied mathematics, or related fields Proficiency in developing and deploying machine learning models (e.g., using Python, R) Experience in data wrangling and feature engineering
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culture, and flow cytometry preparation of deep sequencing libraries for functional genomics experiments bioinformatics, statistics, and programming The candidate must have a strong motivation and interest
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taxonomy and are highly motivated to analyze ecological data statistically. Furthermore, you are scientifically creative and interested in interdisciplinary research with project partners from very different
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. The candidate will have the opportunity to exploit some of the cutting-edge experimental and computational methods, comprising constraint-based and kinetic modeling, statistical analysis of large datasets, high