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Science Programs, and MS in Computer and Information Science (https://cse.aua.am/ ) invite applications for a full-time faculty position in Machine Learning at the rank of Assistant Professor, starting in
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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities
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include the development of finite elements methods, as well as inverse design strategies based on deep-learning and Neural Networks approaches. The latter will then bring the project to the experimental
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) for science with Dr. Aleksandra Ciprijanovic (alexciprijanovic.com) and her research group! The successful candidate will join a multidisciplinary team working at the intersection of deep learning, cosmology
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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architectures for TTS and ASR Entrenamiento de modelos a gran escala utilizando frameworks modernos de deep learning / Training large-scale models using modern deep learning frameworks Publicaciones en
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, from linear models to deep learning, depending on what best fits a given problem. The most successful researchers will be driven by a curiosity for how their contributions fit into the larger picture of
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the number of years post-PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW for purposes
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia
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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying