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modelling knowledge, incorporate reliability/uncertainty, and/or explainable models. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/293458/phd-research-fellow-in-deep
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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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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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) 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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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying
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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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, or related field. Strong programming skills and experience with deep learning models, particularly Large Language Models. Evidence of producing high-quality research outputs. Experience contributing to and
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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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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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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