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to Postdocs, Research Assistants, Research and Teaching Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information. About
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, potentially including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during
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models considering networks of patches and their species and interactions composition to predict spatial and temporal community structure across restoration gradients, aimed at developing a predictive
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, biologists, and engineers. You will also have the opportunity to present your research at international conferences, expanding your network and gaining valuable experience in scientific communication
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requirements. Build appropriate networks within the team. See full advert for full information. Education, qualifications and experience Essential MSc degree in a relevant discipline to the project: social
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and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing . This is a full time and a fixed-term contract (36 months ) based
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technological advances that support the global transition toward net-zero emissions and sustainable aerospace engineering. Motivation The reliability of electric propulsion systems is pivotal for next-generation
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isolation, leading to suboptimal network performance. This project seeks to address this gap by developing a holistic, system-level approach that optimises PST deployment strategies to enhance grid
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challenges in high-speed electrical machine design for electrified transportation and power generation. Together, we will make technological advances that support the global transition toward net-zero
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persists, even for the most powerful sensors operating in this way. A drastic departure from this sensing architecture is “multistatic” radar – enacted by a coherent network of spatially distributed sensors