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this interdisciplinary project, we are looking for a strong candidate to contribute to the development of quantum algorithms and applications, focusing on quantum walks and quantum machine learning on graph structures
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06.12.2021, Wissenschaftliches Personal The professorship of Data Science in Earth Observation is seeking six new PhD candidates/PostDocs for its new center for Machine Learning in Earth Observation
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, seeks a Post-Doctoral Associate or a Research Associate to join a lab focused on applied machine learning. The successful applicant will participate in research involving human computation, knowledge
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Research Associate will contribute to developing and/or applying artificial intelligence, machine learning, and image segmentation and/or data science-based methods to address cutting-edge topics, including
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systems for accelerating computational catalysis and experimental design. The successful candidate will contribute to building AI-native frameworks that combine first-principles modeling, machine-learning
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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Max Planck Institute for Radio Astronomy, Bonn | Bonn, Nordrhein Westfalen | Germany | about 3 hours ago
on the true, astrophysical candidates is a computational needle in a haystack. To tackle these “big data” challenges, astronomers have begun to employ machine learning techniques. The application of machine
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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of Engineering Science. The post is funded by EPSRC and is fixed term to the 31st January 2027. A2I explores core challenges in AI and machine learning to enable robots to robustly and effectively operate in complex, real
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frameworks for quantum machine learning, including conformal quantum prediction and uncertainty quantification in quantum models; Theoretical and algorithmic advances rooted in statistical learning theory