About the Project
In this project, we aim to innovatively integrate semantics-based data modelling and analysis with continuous deep learning to provide a novel effective solution to the above problem.
The semantic data model will provide a machine-understandable foundation for the IoT data and its analysis, and will be able to produce near real-time solution for the detection and correction of IoT data uncertainties. However, this semantic model may be static and imprecise to cope with the highly dynamic nature of IoT systems and the data they have been generating. Therefore, we propose to use deep learning to support the continuous evolution of the semantic model and its data analysis algorithms.
A first degree (at least a 2.1) ideally in Computer Science with a good fundamental knowledge of software engineering or data science, or artificial intelligence or Internet Of Things.
English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.
• Experience of fundamental software design and development
• Competent in design of Internet Of Things applications
• Knowledge of data models and analysis
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management
Some knowledge of machine learning would be beneficial.
Edinburgh Napier University is committed to promoting equality and diversity in our staff and student community https://www.napier.ac.uk/about-us/university-governance/equality-and-diversity-information.
Marie Skłodowska-Curie Actions – European Fellowships Project, Coordinator,
2016-2018, http://www.msca-car.eu/ .
Claus Pahl, Frank Fowley, Pooyan Jamshidi, Daren Fang and Xiaodong Liu,
“A classification and comparison framework for cloud service brokerage
architectures”, IEEE Transactions on Cloud Computing, accepted, online early
access first, DOI: 10.1109/TCC.2016.2537333, 2016.
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