55 phd-mathematical-modelling-population-modelling Postdoctoral positions at Technical University of Munich
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communication system are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random
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(with experiments in the field and in the lab) and modelling techniques. The focus of this postdoctoral position is the generation of empirical datasets for livestock systems in East Africa, and in
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CW and femtosecond optical spectroscopy methods. Preference will be given to candidates who already have some postdoctoral experience, but applications from recently graduated PhDs with an excellent
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will develop into acute or chronic infection. Your expertise - PhD in life sciences, preferably (liver) immunology and/or viral hepatitis. - Experience in high-dimensional flow cytometry for phenotyping
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cooperation with the other scientists is a prerequisite. Your profile: You have a PhD, work experience and several publications in the field of solid oxide cells. In addition, fluent written and spoken English
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. Your qualifications An excellent PhD degree either in Computer Science, Physics, Mathematics or related fields, ideally with a background in quantum theory, quantum computing or quantum machine learning
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competitive dynamics between cell nuclei and how their interaction leads to emergent organism-scale behaviour. You will perform fluorescence microscopy on nuclei populations and quantify their dynamics while
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good publication track record Above-average master’s degree in computer science, electrical/ mechanical engineering, applied mathematics, or a similar engineering-oriented quantitative discipline
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materials science • Extensive knowledge of computer-based modelling and simulation methods in materials science of metals, e. g. Calphad method, precipitation simulation, cellular automata, kinetic Monte
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on the nanoscale (previous works e.g.: https://www.nature.com/articles/s41563-019-0555-5). You will also supervise one PhD student who will work on a complementary topic guaranteeing quick output and an ideal