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Field
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++, or Go, and frameworks like PyTorch or TensorFlow, is highly advantageous. Experience in developing and deploying machine learning models, particularly in natural language processing (NLP) and large
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Bezares (numerical relativity), Dr Stephen Green (gravitational waves, data analysis including machine learning, black holes), Dr Laura Sberna (gravitational waves, black holes, and environmental effects
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. Mentorship will include advancement in time motion and management of a practice. Fellows will learn to work with and lead the entire dental team while providing services to patients with a variety of needs
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: Bayesian Machine Learning – Led by Dr Thang Bui, this project focuses on sequential decision-making and bridging deep learning theory and practice. Applicants with expertise in probabilistic modelling
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testing data Development of machine learning models for battery health assessment and remaining useful life prediction Job Requirements: PhD degree in Electrical Engineering or related subjects. Expert
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integration and AI models tailored for fish behaviour, health, and stress signal analysis. Investigate and apply novel machine learning and deep learning techniques for pattern recognition, classification, and
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well as in a collaborative, team-oriented environment. Preferred Qualifications: Prior experience working with mouse models of cancer is strongly preferred; candidates without prior experience will be
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assimilation, and machine learning frameworks. Expertise in modeling geophysical systems. Strong proficiency in machine learning libraries such as PyTorch. Proficiency in writing clean, efficient, and well
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and future directions within the field of human-computer interaction with a strong overview of how machine learning and/or probabilistic modelling/active inference can be integrated in it. A3 Evidenced
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees