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programming in, for example, R or Python. Particularly valuable is a research background in ecology, biodiversity, systems biology, or related areas, as well as experience working with time-series data, dynamic
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to make a difference. If this sounds like you, you've come to the right place! Experience and technical skills: Research background in at least one of the following topics: simultaneous localization and
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modelling will focus on integrating the nitrogen cycle and GHG fluxes at the field and farm scales, thereby improving our understanding of how different management practices influence the landscape. Your work
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the following: https://wwwen.uni.lu/snt/research/sigcom We're looking for people driven by excellence, excited about innovation, and looking to make a difference. If this sounds like you, you've come to the right
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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publication record in reputable peer-reviewed journals is highly desirable. Proficiency in programming, particularly in R, Python, or other relevant languages, is required. Qualified candidates should also meet
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the ability of different plants to maintain their cellular homeostasis. In the framework of the interdisciplinary research project microC-flux (https://www.wsl.ch/en/projects/diel-c-fluxes-within-needles-from
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, similarities and differences of the studied events Exchange results with the international project partners to investigate the effects of unusual magnetic field configurations on the magnetosphere and atmosphere
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analysis Document and test existing codes Adopt scientific codes for different scientific problems Conduct scientific research in the field of space physics Write manuscripts for publication in peer-reviewed
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); Experience of statistical or other programming languages to manipulate large-scale datasets – e.g. Python, R; Strong quantitative skills and analytical reasoning applied to observational data; A track record