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Doctoral student (m/f/d) in the field of civil engineering, physical engineering, process engineerin
, materials, and analytical sciences. We are looking for talented people to join us. Your responsibilities include: The construction of buildings from wood and the use of insulating materials from renewable raw
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predictions (e.g., Random Forests, Support Vector Machines, K- nearest neighbors) as well as unsupervised ML algorithms for clustering (e.g., K- means clustering, network analysis) will be required. Biological
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documented in a written thesis. References [1] S. Kahl, C. M. Wood, M. Eibl, H. Klinck, BirdNET: A deep learning solution for avian diversity monitoring, Ecological Informatics, Volume 61, 2021, https
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, such as rising CO2, increasing aridity and temperature. Your tasks Participate in joint field campaigns and collect field data and plant/soil samples based on an established network of forest flux stations
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(EnriCo) was established in 2017. In eleven closely linked subprojects, PhD students from forest science, soil science, animal ecology, wildlife science, nature conservation, modelling, economics, and
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Germany with a lively international community. Heidelberg is also a popular tourist destination with its attractive old town and its setting close to the Black Forest and the Rhineland Palatinate. Enjoy
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the 3D Discrete Anisotropic Radiative Transfer (DART) model to disentangle the functional (photosynthetic) and structural (architectural) contributions to forest canopy SIF derived from remote sensing
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to investigate the role of vision and light availability on navigational strategies in wild nocturnal grey mouse lemurs (Microcebus murinus) at the field site of the German Primate Center (DPZ), Kirindy Forest in
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/keras. You are skilled in a range or supervised and unsupervised ML models, for example: unsupervised clustering methods, multivariate linear regression, Random Forest, Gradient Boosting, Support Vector
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externally funded projects: 1. "The exit of states from regional organizations. A comparative analysis" (funded by the German Research Foundation (DFG)) 2. "Performances of Forest Governance of Regional