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KTH Royal Institute of Technology, Scool of Electrical Engineering and Computer Science Job description Cellular morphology reflects fundamental biological processes such as division
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at the Department of Cell and Molecular Biology. The Elf group works across traditional disciplinary boundaries to explore life at the molecular level. We build physical models of key biological processes and develop
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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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, 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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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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scenarios. The research combines field experiments, AI-based analysis of museum specimens, and advanced climate modeling to provide process-based insights into the ecological and economic consequences
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picture recognition. Strong background in machine learning, statistical modeling, and big-data analytics. Experience with infrastructure or transportation data or traffic planning (e.g. micro-simulation
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strategies. The research group focuses on exploration of tumor immune microenvironments through spatial omics and imaging, development of computational models for prediction of molecular and clinical features
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring