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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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(pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models; integration and analysis
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computational biophysics Machine learning and data analysis for biological systems Biomedical imaging and signal processing Molecular modeling and simulations AI applications in bioinformatics or health sciences
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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where
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solutions, focusing on building robust data pipelines, creating efficient machine learning models, and integrating AI capabilities into existing systems to improve efficiency, accuracy, and service quality
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, Computational Linguistics, Machine learning, Computer Engineering or related fields Preferred Qualifications: ● Strong experience implementing and training deep learning models in PyTorch, with attention
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teaching faculty to teach an undergraduate course, Machines that Create, an introductory yet comprehensive overview on Generative AI and Foundation Models, covering the methods and techniques driving modern
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understanding of how acoustic waves are generated and transmitted in wells. The LeDAS project aims to overcome these challenges by combining physical modelling, advanced signal processing, and machine learning in
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new knowledge with a clear academic and social impact, in fields such as AI-enhanced translation workflows, Neural machine translation, large language models, or multilingual NLP, Multimodal and
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reference. The student will focus primarily on the photonic integration of machine learning methods, contributing equally to the development of ML algorithms in this context. Their work will include