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Field
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
students with strong theoretical foundations and a desire to contribute to fundamental algorithmic research. Our group works at the intersection of algorithms, machine learning, and interactive visual
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for imaging plant tissues (rose cuttings) Realisation of a corresponding measuring stand and implementation of test series for imaging Realisation of a data processing routine for the automated detection
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, both written and verbal Knowledge of German and/or a willingness to learn Computer/programming literacy, for example in R, and/or software used in image processing (Adobe Photoshop, ImageJ etc.) Ability
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corpora data correlation. Requirements: A master’s degree in Computational Linguistics, Computer Science or related fields. Solid background in Machine Learning and Natural Language Processing Experience
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Experience in processing remote-sensing information and machine learning is an asset Capacity for interdisciplinary teamwork and excellent communication and presentation skills Ability to communicate in
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• Eukaryotic cell culture and transfection of cell lines • Handling and experimental use of organoid models (provided by a partner institute), including adaptation for biophysical assays and imaging
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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organoids, single cell multiomics, live cell imaging, and animal models. Tasks: to actively pursue the PhD project with the aim of characterizing the molecular and functional mechanisms of epithelial
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after damage. The applied techniques will include e.g. mouse and human kidney organoids, single cell analyses, proteomics, metabolomics, live cell imaging, and animal models. Tasks: to actively pursue
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epithelial cells and neurons in the intestine. The applied techniques will include molecular and cellular biology methods (e.g. neuroepithelial co-cultures, organoid cultures), advanced imaging (e.g