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Field
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-cell transcriptomic atlases for the eyes and optic lobes of three invertebrates with unique and elaborate colour and polarisation visual systems: mantis shrimp, water fleas, and firefly squid
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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thermal camera data as well as visual cameras and lidars. Expanding beyond our previous leading work on odometry, SLAM, and semantic segmentation using 2D and 4D radar data, with this project we wish
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within the University of Münster developing imaging methods allowing to visualize molecular processes inside organisms, tissues and cells. With the help of imaging, we perform cutting-edge research in
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of formulating them, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment
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and spatial omics analyses performing quality control, preprocessing, integration, annotation, visualization, and downstream statistical analyses contributing to data management, workflow development
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, tissue sections, RNA/DNA, tabular data) for predictive modelling using software such as Python Documented experience of neural networks, image processing, deep learning algorithms, and data visualization
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visual methods. By combining a global mapping of key actors, data flows, carbon credits, and financial transactions with in-depth case studies and insights from farmers themselves, the project will provide
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(e.g. odours or visual landmarks). How these cues are sensed and integrated is still poorly understood, especially in nocturnal migratory insects. Work duties The main duties involved in a post-doctoral
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visualization and scientific communication Extensive knowledge of relevant machine learning and AI techniques Exceptional collaborative abilities Self-motivated individual with ability to work independently