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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have
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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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practices” research themes. The successful candidate will have: a PhD in Translation Studies/Machine Translation; practical experience conducting data-driven research in a machine translation/large language
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strong AI focus; a PhD is desirable, but not mandatory Advanced knowledge of machine learning, statistical modeling, and modern AI methodologies Strong programming skills, preferably in Python and common
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infrastructure (e.g. Observatorio del Roque de los Muchachos) Hands-on training in cutting-edge techniques, from detector R&D to advanced data analysis and machine learning. Attendance to international
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of Spanish (not required but valued for teaching and policy dissemination in Spain). Experience with AI-based research workflows, machine learning techniques applied to financial data, or modern
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systems capable of understanding, learning, and acting in complex, dynamic settings. The team works at the intersection of computer vision, multimodal learning, and robotics to create next-generation
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mathematical foundations of data science, machine learning and/or artificial intelligence. Preference will be given to candidates studying either the application of data science/ML/AI to problems in
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analysis and biomedical data analysis, with demonstrated experience in organ segmentation from medical images, using both traditional and machine learning–based methods, and creation of large segmentation
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experience aligned to the goals of at least one of the Centre for Data Science and AI’s programmes with commensurate output. E2 Experience in machine learning and AI, including experience in machine learning