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methodologies, including machine learning, deep learning, TinyML, federated learning, explainable AI (XAI), digital twins, and other emerging techniques relevant to the RGs. Support interdisciplinary research
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development of Northern Ireland. Our core business activities are teaching and learning, widening access to education, research and innovation, and technology and knowledge transfer. - THE ROLE - The role
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machine learning for time series, geospatial data or dynamic models; ideally experience with deep learning frameworks (e.g., PyTorch). Strong analytical and conceptual skills for designing and interpreting
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Computer Science PhD program at the Universitat de Barcelona. https://www.ub.edu/escoladoctorat/en/access-and-admission/access-pathways-and-requirements - Machine/deep learning - Excellent programming skills
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structures, access to space, multidisciplinary design and concurrent engineering, uncertainty treatment and optimisation, machine learning. (https://www.strath.ac.uk/ ) Task description for your Individual
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to Dr Duo Chan. About You Given the interdisciplinary nature of the post, we welcome applications from candidates with a PhD (or equivalent) in Artificial Intelligence/Machine Learning, or climate science
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Machine Learning / Deep Learning / LLMs Other qualifications For the doctoral programme in question, the following are considered as other qualifications: documented knowledge within human-robot interaction
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networks for deep learning on dynamic graphs. arXiv preprint. Trantas et al (2023). Digital twin challenges in biodiversity modelling. Ecological Informatics. Borowiec et al (2022). Deep learning as a tool
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, Computational Linguistics, Machine learning, Computer Engineering or related fields Preferred Qualifications: ● Strong experience implementing and training deep learning models in PyTorch, with attention
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expertise in deep learning. You will be working on a defence funded project with the research focused on the development of multimodal foundation models using data from a broad range of modalities