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past fertility changes can be compared with bulk organic matter parameters that reflect organic matter source and conservation. Job description Integrate in the Earth and Ecosystem Dynamics group, where
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to support a circular bioeconomy. We are seeking a highly motivated postdoctoral researcher to develop and apply machine‑learning and data‑science methods, with a focus on computer vision, to enhance wood
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to estimate annual carbon and water budgets. You hold a PhD in plant physiology, urban ecology, or a related field, and possess strong quantitative and scientific writing skills. You demonstrate excellent
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sensing, computer vision or an equivalent field. You have experience in spatial modelling of habitats with machine learning and the use of large extent Earth Observation data. Experience in artificial
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running codes on HPC clusters). Knowledge of (or prior research) in robotics (e.g., ROS, forward/inverse kinematics), manufacturing (e.g., Additive or Subtractive Manufacturing), or computer vision/language
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experimental methods. Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling. Contribute to biological manuscripts and methods