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microscopy and automated image analysis Basic knowledge of toxicology (e.g. through DGPT training courses or relevant studies / professional experience) Experience in establishing test methods Conscientious
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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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to research this topic. Interest in laboratory work and basic technical understanding. Fluent written and spoken English. Programming skills in e.g. Python, R, Matlab and Java. Experience in image processing
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concerned, among other things, with the effects of images and the mechanisms that make up communication with images. Key questions are: Who produces images and for whom? What message do these images convey
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cyanobacteria and Paramecium bursaria. Image data analysis using AI-based tools and programming analysis scripts. Participation in conferences, presentations, and preparation of publications. Intensive exchange
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functional cell assays Interest in translational leukemia research and innovative imaging and omics technologies Bioinformatics experience, especially in R Ideally initial experience with image analysis, laser
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into goal-directed behavior. We use state-of-the-art approaches including functional brain imaging, automated behavioral analysis, and computational neuroanatomy. We value a collaborative atmosphere, early
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encrypted entries of your Google account and last login time to protect against attacks and data theft from form entries. Lifetime 2 years Name SID Use Used for security purposes to store digitally signed and
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning