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the programme area ‘Plant Quality and Food Security’ (QUALITY). The aim of the research project ‘PhytoM’ is has the goal to mechanistically explore the interaction of phytonutrients in the plant-environment
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is using state of the art machine learning tools to extract interpretable latent dynamics. We seek a highly motivated PhD student to develop a predictive computational model using recurrent neural
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-L EG 13. The position is initially limited to 2 years as a research assistant. lf suitable, an extension via a scientific qualification position (docotor/postdoc/research group leader) or third-party
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an interdisciplinary framework as part of a joint research program. What will be your tasks? We are seeking a highly motivated candidate to join the newly established Collaborative Research Centre “Carbon Sequestration
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founded research area of "Digital Technologies" with a focus on computer-aided high-throughput methods and AI-supported model development presentation of scientific results at international conferences and
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such as ecology, economy and social sciences. ZMT aims to use data science tools, including computer vision and deep learning, for the study of rapid changes in tropical coastal socioecological systems
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program Excellent technical equipment, a wide range of experimental methods and a dynamic and interactive research environment Remuneration according to TV-L, level 13 (65 %), plus annual special payment
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enrolled in a Master’s program in plant sciences, crop science, plant breeding, or a related field. You are interested in fieldwork and data collection. Knowledge or experience with data analysis is an asset
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, computer science, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in Dortmund, we
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this knowledge gap and establish improved GHG models accounting for soil invertebrates. To achieve this, we create a rich AI-training dataset for multi-modal inferences, combining computer-vision, environmental