98 programming-"the"-"DAAD"-"EURAXESS"-"U"-"IDAEA-CSIC"-"IMPRS-ML"-"UCL"-"St" positions at Technical University of Munich in Germany
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and networking opportunities within the Munich AI ecosystem and within structured graduate programs: the Munich Centre for Machine Learning (MCML), the Munich Data Science Institute (MDSI), the local
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in production engineering issues and their investigation Enjoyment of supervising and programming technical systems Purposefulness and independent working style Creativity and willingness to experiment
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fields. Strong programming skills in Python, Java, C++, etc. A solid foundation in generative AI, machine learning, and related areas. Interest in Speech/Language Processing and its application. Know-how
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related discipline. Strong expertise in medical imaging and/or machine learning. Excellent programming and research skills. Interest in translational research and interdisciplinary collaboration with
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Mathematics (analysis, numerics, modeling) or in a comparable program with a strong mathematical focus and knowledge in, for example, functional analysis as well as the theory and numerics of PDEs. Strong
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: Completed master studies in the field of environmental sciences, forestry, landscape ecology, remote sensing or related fields Interested in remote sensing, quantitative methods and programming Prior
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, or a related discipline Interested in climatology/meteorology as well as quantitative methods Prior experience in programming is a plus (e.g., using R or Python) Good communication skills and a high
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listed as the strongest German business school for research, and #9 in Europe for M.Sc. programs in entrepreneurship. The TUM Campus Heilbronn is a dynamic organization whose goal is to achieve excellence
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related field PostDoc track: PhD in a relevant discipline with a record in medical image analysis / ML Strong programming skills (e.g., Python, PyTorch/TensorFlow; image processing frameworks) Background in
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, Machine Learning, or a related field. Excellent programming skills in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow). A solid understanding of recommender systems, deep learning, and