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based at the School of Electronics and Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing
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algorithms and systems. Help with research presentation works such as high-quality paper writing. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or
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to research-based activities, including the development of new data analysis algorithms, processing and analysis of field data, and participation in the fieldwork. Your responsibilities will include: Conduct
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optimization and coordination algorithms for BC Hydro–cVPP interaction and evaluate grid performance through simulated case studies, and analyze service offerings and pricing via case studies and apply social
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goals, formulating contact-rich robot manipulation as a planning problem using haptic-based algorithms Coordinate with the extended research team in hands-on experiments for contact-rich manipulation
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: The successful applicant will be responsible for: Obtaining rigorous mathematical results at the interface of optimization, geometry, and data science Designing, implementing, and testing algorithms Engaging in
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learning, and data science, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models
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experience Evidence of experience with public-patient involvement and stakeholder engagement Track record in scientific publication and dissemination Experience with Machine Learning Algorithms and Artificial
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algorithm that reliably simulates two-phase flow. The PhD projects will be part of developing and analyzing relevant numerical methods and implement them in an open-source framework that will be made openly
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to the development of innovative and sustainable low carbon plastic waste management and recycling solutions. In this project the post holder will develop novel algorithms and methods for analysis of plastic data