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Field
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Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring
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employment with meaningful work and respectful collaboration Comprehensive onboarding and open knowledge exchange within the team Extensive training and further education programs at our UKE Academy
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skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX or PyTorch, is a plus. In addition to above-average interest in the topic
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skills. Experience with programming, preferably Python and R, is required. Experience with mass spectrometry data, in particular metabolomics, and geometric machine learning is a plus. In addition to above
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arise between physical assets and their nominal digital designs, complicating accurate prediction of structural behavior and sustainable lifecycle management. This research aims to overcome
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: Comprehensive training courses and individual opportunities for personal and professional further development Extensive company health management Ideal conditions for balancing work and private life, as well as a
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, NVsim, and others), RISC-V ISA, Programming models, multi- and many-processor systems on chip, approximate computing, and memory-centric computing, circuits & systems based on emerging memory technologies
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or School of Management and participation in the related Graduate School training programs. Qualifications The applicants should possess: an excellent or very-good university degree in economics strong
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partner from data sciences provides data management and AI based Image analysis, an internal simulations group working on quantitative models to reproduce and predict experimental data, and an internal
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this Center: (1) develop a new set of consistent scientific methods for mobility planning and management, (2) integrate a new set of modular metrics for responsible mobility, (3) embed the planning methods