42 computer-algorithm-"UNIS" PhD positions at Technical University of Munich in Germany
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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08.09.2021, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13
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computer vision in dusty conditions by incorporating hyperspectral cameras. In addition, assisting in project applications and general development duties of the Chair. The position is available from
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and Master’s students in Informatics and Data Science. Supervise Bachelor’s and Master’s theses. We Offer Practice-oriented research projects with leading academic and industry partners (like Google
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23.07.2025, Wissenschaftliches Personal The Ecosystem Dynamics and Forest Management Group at the TUM School of Life Sciences, Technical University of Munich studies how forests change in time and space. We quantify these changes, identify their causes and describe their impacts on biodiversity...
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remote sensing imagery (e.g. roof material and condition, wall materials, type of building) Generating results from these algorithms for areas requested by our industry partner Documenting and packaging
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mobility, user demands, wireless system deployment) as well as the digital world (such as networking, memory, computational resources for different applications). Information about the physical and digital
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that are technically well-grounded and at the same time represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster
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project funded within the DFG Priority Programme “Illuminating Gene Functions in the Human Gut Microbiome” (SPP 2474) and be involved in microbiology and molecular microbiology of the gut microbiota
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to the computational complexity of climate models, these will be replaced by physics-informed deep learning surrogates in the aforementioned model coupling. The project will initially focus on one main application