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Job Requirements REQUIRED: PhD in a field of quantitative science; strong background in machine learning and signal processing; experience with and commitment to rigorous and reproducible
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for applications. The ideal candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems
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required. •Experience with deep-learning techniques and libraries welcome. •Past experience in neutrino oscillation and/or LAr TPC experiment welcome. Website for additional job details https
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intelligence, starting 1 April 2027. You will design new sequence-to-expression deep learning models that predict gene expression from DNA sequence in plants, using new experimental datasets generated in
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. The ideal candidate will possess not only a deep conceptual understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. We
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years or longer lab experience Strong programing skills Machine learning and deep learning modeling Expertise in 3-D motion capture systems and biomechanical modeling (OpenSim, Visual 3D, Anybody) Strong
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join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to abdominal imaging. Working alongside
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spectroscopy of molecular beam epitaxy (MBE)-grown thin-film oxides. We seek a deep thinker and rigorous scientist with expertise in two or more of the following areas: thin-film fabrication and molecular beam
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used by a PhD student, likely starting October 2027, to initiate development of a deep-learning architecture. The refined batch of synthetic data will be used by the PhD student to finalise the deep
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Max Planck Institute of Molecular Cell Biology and Genetics, Dresden | Dresden, Sachsen | Germany | about 20 hours ago
Information Theory and Network Analysis Mathematical Biology Mathematical Machine Learning / Theory of Deep Learning Mathematical and Theoretical Physics Stochastic Topology Your qualifications Doctoral degree