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Investigation of learning rules for training networks specifically considering the strong nonlinear dynamics of the neurons Integrated circuit design for the hardware realization of such a network Computational
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06.10.2023, Wissenschaftliches Personal The PhD position is on safety verification of Cyber-Physical Systems at the intersection between control theory and machine learning. The position is full
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private machine learning: Differential privacy (DP) is the gold-standard for privacy protection, but deep learning models trained with DP suffer from privacy-utility trade-offs. You will develop novel model
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Systems, Tübingen/Stuttgart in Germany. Together we want to build a lighthouse for machine learning and modern artificial intelligence in Europe. The cooperation spans all levels, from leading experts
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of the German Armed Forces Munich), the DLR (German Aerospace Center) with its Oberpfaffenhofen institutes, and the BHL, the Bauhaus Luftfahrt. This pooling of research, graduate programmes and teaching merges
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models
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journals. Close collaboration with team members and colleagues. Essential qualifications: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong
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, C++, etc.) Knowledge of machine learning, data mining, or related fields Excellent communication skills and ability to work in a collaborative team environment Interest in social science research
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machine learning technologies. This PhD position is part of the project “Artificial Intelligence for the automated creation of multi-scale digital twins of the built world”, which is funded via the Georg
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environment using machine learning technologies. This PhD position is part of our research on exploiting social media data for earth observation tasks. The work will be on the topic of developing geographically