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theories and numerical methods, carrying out and analysing field and remote sensing observations and conducting and analysing numerical model simulations. The PhD position is funded by the German Research
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workflows for descriptor based microstructure reconstruction to identify material parameters for crystal plasticity simulations from experimental data through inverse analysis to establish structure–property
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approaches in root phenotyping, image analysis, or simulation modeling to understand the functional roles of plant roots in agroecosystems. You have excellent communication skills and will to collaborate with
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reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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the field and greenhouse. You have experience using modern approaches in root phenotyping, image analysis, or simulation modeling to understand the functional roles of plant roots in agroecosystems. You have
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Some experience with numerical simulations such as the Finite Element Method (FEM) and Multi-Body Simulation (MBS) is a plus. We offer: a fascinating understanding at mobility in general with a
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supervision by AI and metabolomics experts at lifespin, as well as by participating professors from the TUM and HSWT. KEY RESPONSIBILITIES Simulating synthetic NMR spectra based on biophysical principles and
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, sense amplifiers or memories Implement and verify circuit layouts through simulation Utilize advanced CMOS technology nodes (28nm, 22nm, and below) Develop behavioural models for circuit verification
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schematics, layout, test benches and simulation Design blocks as Op-amp, LDO, VCO, PLL, voltage or current reference circuits Develop innovative analog design solutions to enhance signal integrity, noise