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companies. Writing well-documented, reproducible code and contributing to open-source Python packages released alongside the methods papers, including close collaboration with the second postdoc on a
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scientific ideas. Previous experience with statistical analysis, programming (e.g., R or Python), machine learning, or genomic data analysis is an advantage but is not required. We welcome applicants who
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acquisition, equipment construction, machine learning, and image analysis. You master programming in for example Python, MATLAB, or a similar platform and you are motivated to further develop your
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, Python, or similar, Land cover and land use change, Multispectral remote sensing, and ideally multi-scale remote sensing tools, Biogeography, ecosystem or biodiversity science, including an understanding
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interest in AI, machine learning, and NLP. • Experience or education in health data science or a health-related field is a plus. • Strong programming skills in Python with the ability to
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socially to the research environment at DIAS and the DDC Code proficiently in R or Python. Be able to communicate project results in English both in writing in academic contexts and to the broader
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high-performance computing, automated workflows and reproducible data analysis; proficiency in Python or a comparable language is expected. A publication record appropriate to career stage that
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: Geospatial and statistical analyses of remote sensing and ecological data, Good programming skills, ideally using R, Python, or similar, Land cover and land use change, Multispectral remote sensing, and
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generative models Is highly proficient in Python, PyTorch and/or JAX Has experience training large-scale neural networks on HPC or GPU clusters Has experience with representation learning and sequence or
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solid academic foundation in quantitative analysis, system modelling, data science or related methods. Experience with programming and data analysis, preferably using Python or a similar programming