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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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computer scientist with programming experience, but no background in science communication - or vice versa - we still encourage you to apply. Your tasks and duties will be a subset of the following
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for industry. To this end, the latest findings from the fields of artificial intelligence, machine learning and cloud-based methods are combined with proven expert knowledge to answer current questions in robot
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | about 1 month ago
holography. We are seeking a highly motivated postdoctor-al researcher to join our multidisciplinary team at the intersection of optics, electronics, machine learning, and atmospheric science. The successful
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)! Tübingen has a long history of academic excellence (founded in 1477; DNA was discovered here ; linked to 11 Nobel laureates) and is an innovation center in medicine and machine learning. About Eberhard
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Curriculum and other under and post graduate degree programmes that involve the Faculty of Medicine. Further, they will be expected to teach in areas outside their specialisation. The successful candidate will
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. Specifically, the PhD candidate is expected to contribute corpora preparation (collection and organizing the annotation), use machine learning approaches for irony detection, and testing for experimental and
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novel machine learning-guided approaches. The position is located at TUM Campus Heilbronn. Your qualifications Strong background in computer science, AI, or related areas or similar fields. Solid
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Max Planck Institute of Molecular Cell Biology and Genetics | Dresden, Sachsen | Germany | 2 days ago
: Dresden, Saxony 01307, Germany [map ] Subject Areas: Artificial Intelligence, Machine Learning, Applied Mathematics, Biomathematics, Mathematical Biology, Computational Science, Statistics, Numerical
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electrochemical impedance spectroscopy (EIS) directly during the disassembly process to classify the cells for their reusability. A pre-trained machine learning model for assessing cell condition based on EIS data