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application-inspired. In our Department System Process Engineering, we are seeking for a Post Doc (m/f/d) – 100 % in non-invasive measurement methods for process and quality control During agricultural-related
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in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages, PyTorch Familiar with foundation models (vision large models or multi
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. To achieve this, we create a rich AI-training dataset for multi-modal inferences, combining computer-vision, environmental parameter measures and DNA data. Your Tasks Participation in fieldwork in Germany
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collaborate closely with a dedicated team of soil fauna experts, ecological data modelers, computer-vision system engineers. Your Tasks Establish data science pipelines, data-modelling strategies, model
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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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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
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annotation, image recognition, data extraction); Development and maintenance of statistical software tools for causal inference and open science applications. Your qualifications profile Enrolment in a
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for a doctoral candidate with the following qualifications: Master's degree in meteorology, physics, mathematics, computer science or an equivalent scientific or mathematical discipline Very good
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on two core but complementary areas: Computer vision and sensor data analysis, applied to tasks such as object detection in drone images (e.g., pest or disease detection), object tracking (e.g. leaves
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such as biology, chemistry, pharmacology, physics, and computer science. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. In the