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prospection with the FLAIR's strengths in developing new multi-objective deep reinforcement learning algorithms, to support decision makers under uncertainty. VUB team:Prof. dr. Ralf Vandam (AMGC), Prof. dr
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reconstruction, data inference and fusion using classical and physics-informed machine learning approaches to advance time-resolved morphological and tissue property imaging of the heart The work will involve
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upholding the University’s Christ‑centered mission and values. · Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, Computer Engineering, Electrical Engineering, Natural
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This project involves the automated generation of textual descriptions for audio content, such as spoken language, sound events, or music. This process typically employs deep learning techniques
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intelligence, data science, medical physics, neuroimaging, bioengineering, or related disciplines, accompanied by accredited training in machine learning, deep learning, or medical image analysis. Experience: A
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deep insight and interest in investigating the interaction between collaborative learning processes in specific knowledge domains in educational sciences and AI tools and infrastructures. Different types
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managing vendors and reducing reliance on external technical partners Strong background in programme governance, risk management, and benefits realisation Deep Oracle Fusion technical knowledge Strong
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Information Technology, the Baldwin Center for Learning and Teaching, Student Affairs, and academic departments and programs. Connection with the campus community in support of teaching, learning, and scholarship is the
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, you will: Acquire and analyse human neuroimaging data, with a primary focus on high-field fMRI of natural sound perception. Develop and apply AI/NeuroAI models, including deep neural networks, to model
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply