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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will
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Full time: 25 Hours per week Fixed term: 12 months We are looking for a candidate to join the University of Edinburgh to conduct research on Machine Learning, Reinforcement Learning, or LLM
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rigorous, collaborative research aligned with project goals. Develop and apply deep learning models, particularly in computer vision, NLP, and multimodal systems. Publish in peer-reviewed journals and
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on a new project called TRUSTLINE, which is part of the Learning Introspective Control (LINC) DARPA Program. The project aims to develop machine learning (ML)--based introspection and monitoring
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properties of Li-rich three-dimensional materials for lithium battery cathodes using density functional theory (DFT), molecular dynamics, cluster expansion, machine learning computational techniques. This work
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2025. We seek to recruit a Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based
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About us: We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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reactions. We welcome applicants from diverse backgrounds, including computational chemistry, bioinformatics, systems biology, and machine learning. The project offers a unique opportunity to collaborate
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with