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focus on developing and calibrating new protein sensors of cytosolic metal availability, which underpin the "metalation calculator ", and expand their applications to different microbes. The work
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make a difference in the world! Position Information We are seeking a Postdoctoral Research Associate to assist the project leader (Dr. Srinivasulu Ale, Professor of Agrohydrology) in research projects
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environmental and geological gradients. The successful postdoc will integrate the scientific community of the Nutribor project. Briefly, the Nutribor project consists of different workpackages. 1/in-situ
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mechanism using surface on in-volume spatial variations. Structural health monitoring (SHM) and smart structures for composite infrastructures: Wireless surface gauges and integrated sensors, SHM/NDT/Inline
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communication and collaborative skills. Experience with SLAM, sensor fusion, LiDAR/depth camera data processing. Familiarity with deep learning for obstacle avoidance (e.g., map-less navigation). Background in
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spectroscopy data, benchmarked against classical DNA profiling. For this, we are now looking for a postdoc who will assemble a database of Raman spectra describing different microbial species, both in bulk and
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive collaborations both with industry and other research groups around the world. The employment
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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nanomaterials to novel cellulose-based textiles. Key responsibilities Conduct a comprehensive sustainability background analysis of different material options for optically adaptive textiles including sensors and