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interdisciplinary cooperation as well as cooperation with existing research groups in mathematical physics, dynamical systems, data science, machine learning, control theory, optimisation, or numerics are expected
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a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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students who would like to write their final thesis in the field of machine learning / computer vision. The primary goal of this master’s thesis is to develop an algorithm that can accurately and efficiently
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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essential. Good programming skills in at least one programming language (e.g., Python). Experience with machine learning, LLMs, or HCI/user study methodologies will be a plus. Strong interest in acquiring and
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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(natural language processing) and machine learning methods. We are looking for very good graduates of Master's programs with a strong interest in topics of accounting or auditing who would like to dedicate
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, as well you know how to work with a computer You work as a team player to solve problems analytically and work out solutions You are willing to learn something new every day and are willing to participate
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, the student will collaborate with researchers who apply data assimilation and machine learning methods to the developed models. Your responsibilities: Analysing a global compilation of paleomagnetic sediment