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pests, or high-throughput phenotyping Solid background in mathematics and scientific programming (R, Python, etc.) along with effective logical reasoning skills Experience with high-performance computing
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, Skills in Python, R, GEE, or other relevant programming environments in the satellite remote sensing field, Experience working with high-resolution imagery and aerial photos, Skills in academic writing and
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various data analysis tasks. Your work will focus on carrying out data preprocessing, data wrangling, and carrying out analyses using R (and sometimes Python). Example projects you will be working
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quantitative genetics or animal breeding Has published high-quality research in peer-reviewed journals Experience with scripting languages (e.g., R, Python, SAS) and/or genetic software (e.g., DMU, ASReml) Can
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of tools such as R, Python, GIS, Git or similar data-science software. Solid experience with community data and biodiversity monitoring. A broad ecological background, ideally including plants and
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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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(preferably with Python). Application procedure Shortlisting is used. This means that after the deadline for applications – and with the assistance from the assessment committee chairman, and the appointment
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Experience with volumetric image data Excellent programming skills (e.g., Python, C++, MATLAB) and familiarity with scientific libraries (ITK/SimpleITK, VTK, TensorFlow/PyTorch, etc.) Ability to work
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programming skills in R and Python. Experience with content coding of verbal descriptions Good communication and teamwork skills. Interest in autobiographical memory and moral psychology. Some experience with
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the following areas. You have a background in tissue-based molecular research and experience with tissue sectioning and the generation and analysis of spatial molecular data. Programming expertise in Python and R