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run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures
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learning is required. Familiarity with some large-scale data analysis is desired. Experience with deep learning frameworks (TensorFlow, PyTorch, Keras, Scikit-learn) and topological data analysis is also
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subject with above-average grades You have very good programming skills in deep learning frameworks and Python Ideally, you already have basic knowledge of computer vision and computer graphics You enjoy
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language as well as experience with machine learning is required. Familiarity with some large-scale data analysis is desired. Experience with deep learning frameworks (TensorFlow, PyTorch, Keras, Scikit
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to submitting your application, please review and update (if necessary) the information in your candidate profile as it will transfer to your application. Job Title: Associate Director, MITS Local Applications
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run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures
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Basic Requirements We are seeking full-time team members to join us at Zhejiang University (ZJU), ideally those who bring a deep passion for education to their work. Ideal candidates should be in
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Université de Lorraine - LORIA : Laboratoire Lorrain de Recherche en Informatique | Nancy, Lorraine | France | about 1 month ago
growing neural gas network learns topologies In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processing Systems 7, pages 625 632. MIT Press, Cambridge MA, 1995. [3] L
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. using programs like PLINK, bigsnpr, regenie, BOLT-LMM, GCTA, LDSC, LDAK, LDpred1/2, PRS-CS, SBayesR, PRSice. Machine learning approaches, e.g. deep learning, autoencoders, XGboost, or penalized regression
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interested in candidates who can leverage machine, deep learning, and statistical methods to monitor species distributions and integrate biodiversity records from multimodal data sources to understand