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and the effects of disordered correlated microstructures on diffusion; iii) development of energy-based models and numerical simulations of hyperuniform assemblies; iv) development and application
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simulation with flotation Presentation of own results at international conferences, in peer-reviewed journal publications and in a PhD thesis Cosupervision of related MSc topics and cooperation with other
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systems for measurement processes, or innovative hardware and software for quantum computers and quantum sensors: we develop tomorrow’s technology in-house for a sustainable and secure society. When will
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for similar processes in other connecting waters of the world ocean. You will start from simulations of an existing model setup with the General Estuarine Transport Model (GETM) and implement quantitative
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promising lean alloy system for additive manufacturing, as the mechanical properties can be tailored through phase composition, distribution and morphology by tuning process parameters. The work is carried
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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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programming background with expierence in using Python, matlab, and/or Java, etc. a good command of German and English, both for teaching and for the preparation of research proposals and publications process
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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