57 evolution "https:" "https:" "https:" "https:" "https:" "https:" "Dublin City University" positions at Cranfield University
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the intersection of ecology, machine learning, and sustainable land management, the research will combine field data collection, deep learning model development, and stakeholder co-design to support biodiversity
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GNSS alone leaves systems vulnerable to interference, spoofing, or outages, particularly in dense urban environments. The development of 6G networks with integrated TN and NTN infrastructures provides
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solutions-focused professional with a strong foundation in IT and a commitment to continuous professional development. With hands-on experience in the Microsoft Power Platform - particularly Power Apps and
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to slower, longer-term, evolution (e.g. vegetation and morphological change). For some, blue spaces may be perceived as ‘wasted space’, whilst highly engineered systems may be perceived as natural
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to ensuring safe, reliable, and high-performance communications. The development of 6G based AI networks with integrated TN and NTN infrastructures provides new opportunities for UAV tracking
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and supplier processes to financial oversight. Alongside this, you will play an important part in the development of HEECA, a voluntary association supporting security in UK higher education
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
life. It also provides valuable skills in AI, materials testing, and data analysis. In addition to its technical innovation, this project offers a range of unique personal and professional development
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how individuals make movement and life-history decisions in dynamic agricultural landscapes. A baseline model and dynamic energy budget framework already exist, but require development to incorporate
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, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide
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. Expected outcomes include: development of novel algorithms that significantly improve predictive accuracy for equipment failure; creation of scalable monitoring systems that reduce operational costs