14 communication-network Fellowship positions at UNIVERSITY OF SOUTHAMPTON in United Kingdom
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have excellent communication skills, alongside a collaborative approach. Open to researchers from around the world and from diverse backgrounds, we are looking for researchers who have the potential
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We are seeking an outstanding researcher with exceptional analytical and communication skills to join our team supporting decisions about the effective and cost-effective use of medicines, medical
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-related area and a keen interest in the field of cancer and/ or gastrointestinal diseases and quality of life. The ability to work independently, as well as excellent inter-personal, communication, problem
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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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interventions for frailty, the development and delivery of a bespoke intervention designed for South Asian communities. The research will use a mixed methods approach to this feasibility study including
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with multiple long-term conditions in the community within our Long-Term Conditions theme. In this programme of work, we will enhance health and well-being of people living with multiple long-term
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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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; Excellent work planning and issue resolution skills; Strong technical, written, and verbal communication skills. Bonus qualifications: Experience with aero low-/mid-/high-fidelity simulation tools for loads
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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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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