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Methodological competence: Strong programming skills, ideally including experience in deep learning and/or high-performance computing Passion for method development Very good English communication skills – both
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weekly working time of 40 hours per week. The position can be filled on a part-time basis. Background: Addressing climate change and biodiversity loss requires a deep understanding of global land-use
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field such as computer science, bioinformatics, mathematics, computational life sciences, or related. Profound knowledge in machine learning, preferably deep learning for image data. Experience in
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environment (e.g. cleanroom, laboratory Deep knowledge of solid state physics and/or quantum information Experience with microfabrication and/or operating and calibrating quantum systems Experience with
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(e.g. via machine learning) to qualitative analyses (e.g. via interviews) to support ambitious policies for climate and energy transitions. This position Green hydrogen is key to decarbonizing many hard
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focus on deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning
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background in a technical field such as computer science, bioinformatics, mathematics, computational life sciences or related. Profound knowledge in machine learning, preferably deep learning for image data. A
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machine and deep learning Programming experience with Python and Pytorch Strong analytical and problem-solving skills Excellent communication & interdisciplinary skills Fluency in English (written and