27 experimental-fluid-mechanics PhD positions at Technical University of Munich in Germany
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the impact of recurrent droughts on the carbon allocation by trees to the rhizosphere and related processes, as well as its effect on soil microbial diversity. The experimental forest site is a throughfall
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cellular biology, the project will investigate signal transduction mechanisms at the protein and membrane level. Experimental systems will include 2D cell culture, organoid models, and advanced biophysical
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digital twin systems Participation in research projects, execution of project deliverables, and generation of reports Maintain and develop the live testbed infrastructure for real-world experimentation and
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-selectivity. Calculations and simulations will guide fabrication of experimental prototypes, to be tested in beamtime experiments at world-leading neutron science facilities (e.g. ILL, FRM II). Experimental
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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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research • great ambition and motivation to work on complex research questions by applying cutting edge experimental methodologies We are seeking a creative, passionate individual with excellent
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“Health and Usage Monitoring System” (HUMS), a data-based as well as a physics-based approach is being investigated using the example of rotor components of the Leonardo AW169 helicopter. Experimental
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these materials into high-performance fibers and functional materials. By manipulating molecular interactions through chemical and physical methods, we tailor the structural and mechanical properties of bio-based
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28.07.2023, Wissenschaftliches Personal Prof. Karen Alim’s group on Biological Physics and Morphogenesis at the TUM Campus Garching uses theoretical and experimental methods to investigate how flow
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main focus on the development of control software. ▪ You will design and implement advanced control and readout protocols and optimize experimental characterization workflow,s leveraging machine learning