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project focused on developing an advanced machine learning framework for spatio-temporal datasets. The position is for 2.5 years and is partially funded by the Dutch Research Council (NWO) through
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Trustworthy Graph Machine Learning for Population Scale Networks Job description We invite applications for a postdoctoral researcher to work on fundamental techniques for trustworthy graph machine
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Hey machine learning enthusiast with a love for physics and
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Hey machine learning enthusiast with a love for physics and complex systems, will you help us develop a new generation of road traffic prediction methods? Job description Road traffic is a highly
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, subjective user feedback, and environmental data. The research will involve machine learning, human-centred experimentation, real-time comfort prediction, and the integration of intelligent climate control
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, and environmental data. The research will involve machine learning, human-centred experimentation, real-time comfort prediction, and the integration of intelligent climate control systems into vehicle
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Physics Informed Machine Learning method which exploits the advantages of physics-based and data-driven models, while mitigating the disadvantages. This research will contain experimental and modelling
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
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Specific Requirements The ideal candidate has the following qualifications: - interest in human cognition - experience with neural data, especially EEG or MEG - experience with machine learning models
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your academic career? Your interests lie in the field of machine learning techniques, particularly artificial neural networks, and deep learning? And you would like to continue your research