56 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"SciLifeLab" positions at Cranfield University in United Kingdom
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performance degradations and unwarranted system failures can occur. There is certain physical information known a priori in such aerospace platform operations. The main research hypothesis to be tested in
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, finance, and healthcare, where data integrity and system reliability are non-negotiable. This PhD project addresses the integration of robust security measures within AI-enabled electronic systems
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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other relevant information related to daily menus Follow all Food Handling and Hygiene Guideline Regulations at all times Carry out any other reasonable task as requested by a member of the management
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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training will be provided. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods. There is flexibility to tailor the research to your
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should have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge
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will have sound knowledge of data protection regulations and be experienced in co-ordinating events and activities. Experience of working within an Apprenticeship or Higher Education environment and
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap