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PhD Scholarship – Structural Aspects of Digestion Job No.: 679367 Location: Parkville campus Employment Type: Full-time Duration: 3.5-year fixed-term appointment Remuneration: The successful
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ADMISSION to HDR Those who apply for the scholarship opportunity that have not already applied for admission to a relevant HDR must also apply. Instructions on how to apply for admission can be found here: https://www.flinders.edu.au/study/apply/apply-research-degree/how-to-apply By lodging...
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an ARC Linkage Project focused on developing an autonomous system for detecting and quantifying structural damage in infrastructures (e.g., bridges, grain silos) using computer vision, digital twins, and
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the area of structural health monitoring of civil engineering structures on an Australian Research Council Future Fellowship project “Innovative Data Driven Techniques for Structural Condition Monitoring
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THE UNIVERSITY OF LIVERPOOL FACULTY OF SCIENCE AND ENGINEERING SCHOOL OF PHYSICAL SCIENCES DEPARTMENT OF CHEMISTRY POSTDOCTORAL RESEARCH ASSOCIATE IN THEORETICAL CHEMISTRY GRADE 7, £39,355 – £45,413
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to the damage tolerance design of structures. Cyclic loading causes adverse effects and leads to accumulated damage and degradation of residual strength in composite laminates. Predicting the residual strength
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: Bachelor degree (first-class honours or upper second-class honours) in Civil and Structural Engineering or related fields. Language requirement: IELTS, Overall 6.5, Speaking, Writing, Reading and Listening
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the area of structural health monitoring of civil engineering structures on an Australian Research Council Early Career Industry Fellowship project titled, 'Transforming Smart Bridge Monitoring by Computer
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PhD Scholarship in the Centre for Advanced Defence Structures and Materials Experimentation (CADSME)
structures and/or materials experimentation. The project would suit students with a background/interest in advanced structures and/or materials experimentation.
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. Through choreographed interactions with movement experts, this project expects to generate machine learning strategies to understand how people and robots can reliably and fluently move together. Expected