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position: Postdoctoral Research Fellow for AI in Education: Multimodal Learning Analytics in Collaborative Learning (m/f/d, E 13 TVL, 100% for 2 years) We are seeking a highly motivated postdoctoral
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Technology and is closely connected to the Birkbeck Centre for Creative AI and the Birkbeck Immersive Learning Centre. Together these centres bring expertise in art history, museum studies, creative AI
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organic methodology. Key Responsibilities: Conduct routine lab research and data analysis in asymmetric transition metal catalysis. Mentor junior researchers and teach organic chemistry lab skills, oversee
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research infrastructure. Apply advanced statistical, machine learning and data engineering methodologies to large-scale, longitudinal datasets, contributing to innovative melanoma and skin cancer research
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sustainable manner. Key Responsibilities: Responsible for developing explainable machine learning algorithms for Tunnel Boring Machine (TBM) tunnelling and excavation Developing large language model enhanced
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-driven and externally relevant. Our curiosity about languages, histories and global cultures drives our insight, research and our learning. For any informal queries about the role or the team, please
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teach on one of the leading MSc programmes in the field and to supervise MSc theses, in collaboration with external partners, as appropriate. What we can offer you: The opportunity to continue your career
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top-tier conferences and journals related to AI, security, privacy, digital forensics, or trustworthy computing. Solid background in Machine Learning, Digital Forensics, Security, and AI Generation
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Stephen & Denise Adams Center for Parkinson's Disease at Yale School of Medicine | New Haven, Connecticut | United States | about 2 months ago
-generation AI models of gene regulation and disease progression. Our work is redefining Parkinson’s disease biology and enabling translational breakthroughs. The role Develop deep learning models across genome
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neuroimaging methods to better detect disrupted function following neonatal brain injury, and identify new more energy efficient learning algorithms that could reduce the economic and environmental cost of AI