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to state-of-the-art laboratories, hardware/software resources, and design facilities, supporting AI-powered electronics research. This project will be conducted within Cranfield’s Integrated Vehicle Health
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hazards and assessing their risk for the society. At the same time, they are fully qualified users of remote sensing, GIS and statistictical software techniques that can be applied to geoscience and
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: Characterize recycled aluminium microstructures using SEM/EBSD, microCT, and in situ mechanical tests. Develop microstructure informed models of plasticity and fracture across length scales. Investigate how
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, to test the transferability of atlas-based predictions. This project moves beyond single patient-specific studies to a systematic, mechanistic understanding of how geometry drives atherogenic flow. It
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edge AI hardware/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a
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/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a scenario generator for
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to, contributing to teaching, laboratory and exercise teaching, supervision, and examination work within the employee's areas of competence. Be prepared for changes to your work duties after employment. Required
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: Fail safe, Distributed Digital Twin for Innovative Air Mobility (IAM) Operations Supervisor: Prof. Dr. Uwe Assmann, Chair of Software Technology and co-supervised by at least one
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software development in relevant applications, including system analysis, design, implementation, testing and deployment. Candidates with limited experience in exoplanetary science but with strong relevant
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and/or testing of samples. May occasionally instruct others in basic laboratory techniques. Working Conditions: Work is performed on-site in Cambridge, MA. May be required to work with a variety of