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Your Job: In this position, you will be an active part of our Simulation and Data Lab for Applied Machine Learning. Within national and European projects, you will drive the development of cutting
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deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. We are seeking a researcher to join our team in an ERC project on DNA data storage
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work schedules tailored to your needs as well as remote work are key parts of our work culture. An exciting and varied job where you will gain deep insights into a research institute as well as national
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2019, unites top PhD students in all areas of data-driven research and technology, including scalable storage, stream processing, data cleaning, machine learning and deep learning, text processing, data
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synchronizing ultra-short laser pulses with ultra-fast beam blanker and detectors Analyzing the interaction of excited carriers with shallow and deep defects by measuring charge transition levels Investigation
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position with a weekly working time of 30 hours per week (75%). The PhD position is funded by Deutsche Forschungsgemeinschaft (DFG) and related to the major multi-national initiative “Deep Dust” supported by
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deep-rooted inequalities. Complexity arises in defining transport equity, as the choice of distributive principle significantly influences its measurement and outcomes. Therefore, it is still challenging
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collection programs (e.g. R, fomr) Very good English and German skills (or willingness to acquire them) Interest in media communication We offer Part of a young and dynamic Emmy Noether Research Group Diverse
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models. Your tasks: Research, development, and evaluation of Machine Learning and Deep Learning methods Prototype development Literature review Publication and presentation of scientific results in
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with data from animal communication can be acquired on the job but require willingness to learn. The ability to work in a diverse, multi-national team is required. The project offers a cohesive and