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. Potential methodological topics focus on meta-analyses and the analysis of large-scale assessment data: Methods and approaches to synthesize large data sets via meta-analyses (e.g., meta-analyses of large
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information of the data to make a prediction using advanced mathematical tools. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms
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develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address major challenges in important applications including marine domain and
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mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in
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. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in English Desired qualifications: Experience with research on epidemiological modelling, with an emphasis
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. The candidate further develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address major challenges in important applications including
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. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address
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technological progress in our increasingly digital, data-driven world. Researchers in Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By
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-time control & optimization algorithms. Pilot demonstrations on partner radio telescope facilities such as APEX and the Sardinia Radio Telescope may be possible, depending on the interest of the PhD
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should not exceed one page Desired qualifications: Good knowledge in programming language theory, algorithms, distributed systems and logic Experience with language-based techniques for information-flow