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expertise in HCI and education, including adaptive gamification, engagement, learning analysis, and the design of motivational affordances in education. As part of the project, the PhD student will work with
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doctorate, contains: Reinforcement Learning for Public Health Decision Support in Emerging Arbovirus Prevention The Artificial Intelligence Lab in the Department of Computer Science at Vrije Universiteit
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machine learning approaches Collaborating with experimental and clinical research partners Support and preparation of scientific reports and journal articles
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explore how goal-driven AI agents can generate and execute robot programmes automatically while learning from and collaborating with human partners. The research will investigate hybrid approaches
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description Integreat - the Norwegian Centre for Knowledge-driven Machine Learning at the University of Oslo invites applications for a doctoral research fellowship. The PhD candidate will work at the
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and Machine Learning Researcher will report to Ayan Paul, Research Scientist at EAI. There will also be opportunities to work on industry collaborations. Responsibilities will include building an ETL
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We are looking for a highly motivated PhD candidate interested in AI-based methods, including machine learning and language technologies, for the integration and analysis of clinical, advanced data
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working on robust methods for statistical learning
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applied mathematical modelling machine learning multi-fidelity modelling numerical methods. Demonstrated programming ability (MATLAB/Python/C++) and enthusiasm to learn PyTorch. Previous experience in one
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 9 days ago
macromolecular dynamics, as well as collaborations with the European MDDB initiative. The candidate will develop new graph transformer architectures to learn conformational heterogeneity from molecular dynamics