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, graph theory, satisfiability problems, discrete optimization. Strong interests in chemistry as well as proven competences in programming and ease with formal thinking are a necessity. This PhD project is
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integrated, holistic planning and scheduling to optimize resilience across several factory systems. Development of AI-based tools and complex simulation systems is expected to be a core contribution. 3) IT and
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, 107 Pd, 147 Pm, 151 Sm) from environmental samples (e.g. seawater, sediment, soil) and radioactive waste samples (e.g., spent resin, steel, concrete) Develop and optimize radiometric methods
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within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and with
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infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs offer computational efficiency and
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: A master's degree in data science/AI, Mathematics, Electrical and Computer Engineering, or a related field. Strong background in machine learning, data science, and optimization theory. Desired
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defects. The charge transport will be implemented stochastically to mimic nature. A significant focus of the project will be to apply machine learning techniques to optimize the model and enable charge
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contemporary data-driven techniques. Computational methods such as optimization, filtering algorithms, predictors, etc. Software and coding skills with, e.g., Python, MATLAB, R, C++, Julia, potentially HIL
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contemporary data-driven techniques. Computational methods such as optimization, filtering algorithms, predictors, etc. Software and coding skills with, e.g., Python, MATLAB, R, C++, Julia, potentially HIL
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processing, optimization, or information theory; Experience in programming, e.g., in C++, Python or Matlab. Experience with quantum simulators, such as NetSquid, is a plus; Familiarity with the basic concepts