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existing technologies, right through to the tested prototype. The Data-based Methods team at Fraunhofer ENAS develops real-world applications using AI, machine learning and computer vision. The main focus is
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in machine learning. A core research of the group is the intersection of explainability, fairness and robustness of machine learning models. Become a part of our team and join us on our journey
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-of-distribution objects in a given scene. Using machine learning methods for such vision usecases can improve efficiency. To employ machine learning methods in safety critical usecases, it is essential to be able
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for cutting-edge research in various fields including robotics, machine learning and systems intelligence. An exceptional opportunity to experience research in a highly inspiring international environment
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Max Planck Computing and Data Facility (MPCDF), Garching | Garching an der Alz, Bayern | Germany | about 2 hours ago
performance computing (HPC), artificial intelligence and machine learning, as well as in the design and implementation of solutions for data-intensive projects in close collaboration with the Max Planck
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complexity of neural representations based on partial information decomposition, Transactions on Machine Learning Research 5
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in your field of study Flexible working hours allow you to combine your studies and professional practice in the best possible way Very good travel options, whether by bus or car Exciting seminars
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Max Planck Institute for the Study of Crime, Security and Law, Freiburg | Freiburg im Breisgau, Baden W rttemberg | Germany | about 2 hours ago
dynamic research environment in which criminologists, psychologists, sociologists, mathematicians, and computer scientists work together to understand the causes and consequences of criminal behavior and to
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, the Max Planck Institute for Biogeochemistry houses a unique and flexible research program that grants German and foreign students a broad selection of learning opportunities while still maintaining a
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the characterization of these components on 300mm silicon wafers, large amounts of data are generated by extensive physical measurements. An efficient preparation of data is therefore crucial for short learning cycles