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or for multiobjective optimization problems. Implement the developed algorithms (e.g., in Python) and evaluate their practical performance on artificial and/or real-world data. Teach tutorials (in English) for
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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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with chemoreception and sensory biological techniques (SSR, GC-EAD, EAG). •Experience in analytical chemistry (GC-FID, GC-MS). •Experience in or willingness to learn statistical data analyses, data
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astrophysics, condensed matter physics and solid state theory, statistical and biological physics, mathematical physics, quantum information theory, and nuclear physics. Course organisation All graduate studies
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ecology (e.g. pollination, chemical or molecular ecology). • Strong experience with statistical data analyses. • Experience in or willingness to learn analytical chemical analyses and data processing
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mathematics, (theoretical) computer science, machine learning foundations, electrical engineering, information theory, cryptography, statistics or a related field. - Advanced knowledge of probability theory