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• Experimental investigations using atomic force microscopy (AFM), including force-distance curve-based imaging, (single molecule) force spectroscopy, nanomechanics, and associated data analysis • Application
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the perspective towards generating data for entrepreneurial seed funding (GO-Bio etc.) for a further development of the technology towards the market. In that sense we also highly encourage candidates that would be
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the objectives more precise; working on the individual PhD study project with its focus on the methodological contributions as well as on empirical data processing for the case study analysis in collaboration with
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persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When you apply for a position with the
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its rich information content, conventional analysis methods have not yet fully realized its potential. This research project aims to develop a robust AI foundation model based on modern Transformer
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accessible to users from science and industry Your qualifications: ■ Master’s or equivalent graduate degree in computer science, artificial intelligence, machine learning, mathematics, statistics, data science
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in-situ experimental data to the landscape scale. Doing so, you will address questions of climate change impacts on meteorological extremes, phenology of selected forest tree and animal species and
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. • Collaborate with project partners. • Contribute to the preparation and analysis of data for publication and presentations. The following qualifications will be of advantage but are not stringently required
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motivated PhD students to strengthen our interactive and collaborative team. The projects are founded on the well-established and highly visible track record of the laboratory in the analysis of plant growth
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. Hence, you will have substantial freedom to define projects. We focus on methods-driven ML for scientific modeling, currently emphasizing the integration of data-driven and mechanistic approaches