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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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specializes in computer science and, more broadly, in information and communication sciences and technologies (ICST). Its research activities are organized around several major axes: the modeling and
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), ecological processes (primary productivity and decomposition rates), and greenhouse gases (3D-printed flux chambers), and investigating how we can use citizen science-driven data streams for model development
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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other empirical damage and vulnerability data. Couple the ABM with the Regional Flood Model (RFM) to describe temporal developments of flood risk considering adaptation decisions. Different adaptation
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a powerful way for assessing forest stress and disturbances over large areas and to monitor forest vitality over time. This research uses remote sensing technologies together with physical models and
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. Project background The position is associated to a project on phase-field modeling of fracture. The PhD project aims at developing cutting edge models for the fracture behavior of quasi-brittle materials
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to regenerative agriculture, including biological indicators that are often neglected in soil health monitoring programs. You will be part of a team of PhDs focusing on modeling soil processes at different scales
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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of the canopy and longer wavelengths being most sensitive to the characteristics of trunks and soils. Recent research has shown that phase difference between two temporally separated SAR acquisitions is also