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15.04.2025, Wissenschaftliches Personal The Lab for Artificial Intelligence in Medical Imaging (www.ai-med.de) is inviting applications for a fully funded PhD position in interpretable machine learning for dementia prediction. see here:...
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qualitative methods; some background in or willingness to learn computational social science methods as well as R and/or Python is highly desirable. Willingness to develop an ambitious empirical research
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05.02.2025, Wissenschaftliches Personal PhD position (E13) is available at the Department of Computer Science a the TUM focusing on online learning and game theory. Text in English and German (below
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or computational science Strong mathematical skills and interest in developing new mathematical methods Good knowledge of mathematical/numerical optimization methods or deep learning methods Enthusiasm
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The position is available as soon as possible. We offer thorough training in: - Performing a wide spectrum of molecular, cell biology-related and computational methodologies and techniques, such as primary cell
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interdisciplinary, international, and in-depth, but also practical. We offer a possibility to obtain broad and profound expertise, both theory and practice, in the field of agricultural applications, technology and
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distinguish you both professionally and personally. • You have obtained very good diploma or master’s degree in forest sciences, economics, environmental science, geo-information science, landscape ecology
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(https://soilsystems.net/ ), a Priority Programme (SPP 2322) funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation). Within SoilSystems, scientists from different disciplines from
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at conferences. The PhD program has a duration of three years and with the PhD degree offered by TUM. Qualifications A Master’s degree in Operations Management, Computer Science, Industrial Engineering, Economics
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profile: • Very good degree (Master or Diploma) in aerospace engineering, mechanical engineering, computer science or a comparable field. • Experience in machine elements, structural analysis, fault