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learning methods, with strong analytical and programming skills (e.g. R or Python) Experience of undertaking accurate, reproducible data preparation and analysis A track record of peer-reviewed research
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Excellent written and spoken English skills The following will be considered an advantage: Programming and data analysis skills (e.g. R, Python, or equivalent tools) Good written and spoken Finnish skills
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programming, data handling, quantitative methods, and confident research use of Python, R, Git, and Jupyter—is a baseline for the appointee, not the position’s principal teaching focus. This is not a position
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and/or Python; experience with Unix/Linux environments and bash is considered an advantage Prior experience in the preprocessing, analysis and interpretation of omics data and a strong interest in
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(e.g., laser scanning, optical satellite imagery, radar, UAV, or aerial imaging). Strong programming and data analysis skills (e.g., Python and/or R), as well as proficiency in GIS software and
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perception at the landscape scale under different scenarios and employ behavioural experiments and Big Data analytics to understand how changes in time perception influence pro-environmental and sustainable
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perception, and validate experimental results using real-world data obtained with citizen science. The project will also model the effects of nature on time perception at the landscape scale under different
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science who are curious about applied machine learning in the natural sciences. Prior machine learning or Python experience is a strong bonus, but not a must. We seek colleagues who enjoy coding, scripting
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or programming software (e.g. Stata, SAS, R or Python) Experience of advanced longitudinal modelling, including time-to-event models, mediation analysis and causal inference approaches Excellent communication
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certificate, IELTS or TOEFL). Programming skills (e.g., Python, C++, Rust). Interest in collaborative research with both academia and industry. Qualification requirements The eligibility criteria