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correction and/or mitigation. Knowledge about networking protocols and distributed algorithms. Experience in programming, e.g., in C++, Python or Matlab. Experience with quantum simulators, such as NetSquid
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assessment, S-LCA, or LCC Programming or scripting capabilities in Python, R, MATLAB, or similar Experience with data processing, model automation, or simulation tools Interest in contributing to teaching
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@au.dk) Applicants must have a relevant PhD degree in biology, biogeochemistry, hydrology, glaciology, oceanography, geoscience or physics. Field experience, data analysis and programming (e.g., python
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should have the following qualifications: Experience in software development, programming with Python, C++, etc. Good/strong knowledge of robot operating systems, robotic control and virtual
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Research associate (ATAP) in Mass Spectrometry and Protein Research - University of Southern Denmark
analysis is highly prioritized, including also programming skills in R and/or Python. Personal Competencies Independence and Creativity: Ability to work independently and creatively to solve complex
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optical systems with COMSOL, Lumerical, or similar. Experience with control software and data logging – preferably in python. Good communication skills and a structured approach to work. Why Us? Joining
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and applied statistics, including use of tools such as R, Python, GIS, Git or similar data-science software. Solid experience with community data and biodiversity monitoring. A broad ecological
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qualifications: Strong experience in programming using Python, R, or other languages Research experience in remote sensing of cover crop, crop type classification, and crop biomass Insight into global
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, the candidate should have solid programming skills (e.g. Python, Julia) and an excellent command of English. You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level
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/leveraging large public databases (knowledge of working with public APIs) are advantages although not a must. Proficiency in Python and/or R in scientific computing and reproducible data analysis is a must