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climate data, simple physics-based models, and AI to deliver more accurate projections of how our climate will warm and recover in a net-zero future. As part of this project, you will contribute to develop
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transmitted by 50%, which will contribute to the creation of Microsoft Azure’s next generation ultrafast datacentres and optical networks. The project aims to address research as well as practical aspects
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calculations; Experience with developing, training, and optimizing neural networks or other machine learning models. For this position we are targeting a salary corresponding to Level 4 Spine Point 28 - 30
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and networking activities. There is an opportunity for one competitively appointed facilitator in one of the three research sites to receive additional academic training opportunities. The employing
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Engineering at Southampton you will benefit from the extensive network of collaborations with members of the group. There will be an opportunity to guide PhD and Master students. Funding is available initially
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expertise spanning mooring lines, floating platforms and anchors, with both academic and industry perspectives. You will have many opportunities to expand your network, both in academia and industry, through
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of symmetries and equivariance and their implementation in deep neural networks, mathematical criteria stability and robustness of neural networks, applications of topology and geometry to deep
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member of the Energy Technology Group within Mechanical Engineering at Southampton you will benefit from the extensive network of collaborations with members of the group. There will be an opportunity
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opportunities to expand your network, both in academia and industry, through project meetings, workshops and conferences. You will contribute to the activities of the Intelligent and Resilient Ocean Engineering
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systems, with emphasis on criteria for robustness, reliability, and stability. Topics of interest include the study of symmetries and equivariance and their implementation in deep neural networks