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We are seeking a highly motivated researcher to conduct research in modelling and monitoring forest growth. About the position The projects focus on developing models for forest types and tree
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area: Biology, with a focus on wetland ecology, biodiversity, and ecosystem services Admission requirements: Biology graduates and master's degree holders who are, on the scholarship start date, duly
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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | 14 days ago
-forms back to NH3 with the use of green electrons and water can contribute to fixed-N recycling concepts and inhibit the accumulation of reactive-N-species in the ecosystem. Meanwhile, the increasing
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approach including empirical data analysis, experiments, and theoretical modelling to develop science-based management strategies for the restoration of woodland ecosystems. We will collect, and collate from
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susceptible to SM, VWC, and atmospheric delay. As a result, the objective of this PhD project is to develop models able to fuse backscattering and phase information to estimate SM and VWC more accurately. The
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an independent impact assessment of potential climate interventions in the Arctic marine environment through laboratory experiments and computer modelling. The team will develop physical, climate and ecosystem
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species like birds and bats. Combining PAM with occupancy modeling allows for large-scale ecological studies, assessing both individual species and community responses to environmental changes. In this 4
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complete picture of fish habitat use and connectivity. The PhD is part of the section for Ecosystem based Marine Management and the Marine Habitats research group, as well as several synergistic initiatives
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in bioacoustic technology, such as passive acoustic monitoring (PAM), now enable efficient study of vocalizing species like birds and bats. Combining PAM with occupancy modeling allows for large-scale
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analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc