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25.07.2025, Wissenschaftliches Personal Concerning a full position (100 %), we are hiring a Research assistant (f/m/d) at the Chair of Non-destructive Testing in the Project ScanPyramids OUR PROFILE
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Description Who We Are: The Cutsail research group at Ludwig-Maximilians-Universität (LMU) Munich is seeking a highly motivated PhD student / research assistant for full-time, on-site work in the
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Description As a humanistic, sustainable and action-oriented university, Leuphana University Lüneburg stands for innovation in education and science. Methodological diversity, interdisciplinary collaboration, transdisciplinary cooperation with practice and an overall dynamic development...
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Description Who We Are: The Cutsail research group at Ludwig-Maximilians-Universität (LMU) Munich is seeking a highly motivated PhD student / research assistant for full-time, on-site work in the
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smart technologies in the bioeconomy, agricultural, food and environmental policy impact analysis, and/or production efficiency and food markets. leading to an internationally competitive PhD degree and
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for a research assistant (m/f/d) - pay grade 13 TV-L, 65% for a limited period of three years. Your tasks: You will be working on a multidisciplinary project that aims to develop and test an mHealth
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” (funded by the German Research Foundation, DFG). PhD Researcher in Linguistics (m/f/d, E13 TV-L, 75%) The position begins on 01 October 2025 and ends on 30 June 2029. The position is associated with project
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measuring stand for the collection of hyperspectral data on a test field Collection of imaging data and creation of a database Establishment of a data processing routine for pre-processing the hyperspectral
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for the induction of abiotic and biotic stress factors in a defined, compact test system (Phenowell®) on Arabidopsis thaliana Multimodal recording of stress symptoms using the following and possibly other methods: o
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. Specifically, the PhD candidate is expected to contribute corpora preparation (collection and organizing the annotation), use machine learning approaches for irony detection, and testing for experimental and