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Degradable Microneedle Sensors with Bioadhesive Hydrogels for Sustainable Oral Glucose Monitoring (S3.5-DEN-Neves) School of Clinical Dentistry PhD Research Project Competition Funded Students
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Photonics for the scale up of quantum sensors (S3.5-ELE-Smith) School of Electrical and Electronic Engineering PhD Research Project Competition Funded Students Worldwide Dr Joe Smith, Dr Kristian
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Computational Astrochemistry/Algorithm development for Quantum Dynamics Calculations School of Mathematical and Physical Sciences PhD Research Project Self Funded Prof AJHM Meijer Application
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are urgently needed to monitor PFAS in water and probe their interactions with biological systems. This PhD project will develop a cutting-edge single-molecule optical sensor for real-time, ultra-sensitive PFAS
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An exciting opportunity has arisen for a Research Software Engineer to join our team and the Green Algorithms Initiative, one of the leading academic teams in the field of sustainable computing
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Teaching Faculty. The Undergraduate Division is seeking teaching faculty at all levels with expertise in either Data Structures, Algorithm Design and Analysis or Python Programming. About MBZUAI – A Global
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adaptation of the mesh during simulation to resolve and track features in the flow. The focus of your PhD would be on developing novel algorithms to efficiently redistribute and rebalance the parallel
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to development and climate change. This project will analyse data from innovative motion sensors and a suite of other sensors deployed along the Alaknanda River, a tributary of the Ganges in India, since 2025
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Digital and sensor based conformance validation for large scale forged components (C4-AMR-Crawforth)
Digital and sensor based conformance validation for large scale forged components (C4-AMR-Crawforth) Advanced Manufacturing Research Centre PhD Research Project Competition Funded Students Worldwide
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research directions include: Develop a novel microenvironment classification algorithm by extending existing methods Model indoor air quality levels in London microenvironments by combining machine learning