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  Re-advertisement: Real-Time IoT Analytics at Edge


   Department of Electrical and Computer Engineering

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  Assoc Prof Qi Zhang, Assoc Prof Panagiotis Karras  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

For online application details, please visit Aarhus University PhD graduate school webpage: https://phd.tech.au.dk/for-applicants/apply-here/saeropslag/re-advertisement-real-time-iot-analytics-at-edge-1

Research area and project description:

The proliferation of the Internet of Things (IoT) and the ever-increasing massive IoT data provide unprecedented opportunities for innovations. How to extract value from the massive IoT data has gained significant interest among researchers and industry. Conventionally, historical analytics is often used to obtain insights from the mining of historical data for diagnostic and descriptive purposes. On the other hand, real-time IoT analytics promises to realize proactive and predictive analytics by analyzing IoT data as soon as it enters the system in a predefined timeframe; that is becoming a new trend in IoT data analytics and has applications in diverse verticals, such as smart home, industrial IoT, smart grid, E-health, smart transportation and many others. Real-time IoT analytics at the network edge would significantly reduce the analytics response time and save the bandwidth to forward all the data to the cloud. However, the analytics capability of edge computing is not as powerful as that of cloud computing. Therefore, the question is not how to perform analytics on massive IoT data, but rather how to perform analytics on the right data.

Qualifications and specific competences:

We are looking for highly motivated and independent students willing to take the challenge to do a successful 3-year PhD programme in Aarhus University. The ideal candidate will have the following profile (but not all items are required for a successful application):

  • Relevant Master’s degree (e.g., Computer Engineering, Computer Science, Software Engineering, Electrical Engineering).
  • Excellent undergraduate and master degree grades are required.
  • Background on data analytics, machine learning, information theory, data storage is highly desired, but candidates from other disciplines will be considered based on their merits and potential.
  • Background on linear algebra, mathematics and statistics is desired.
  • Strong programming skills in Python or C++.
  • Good English verbal and written skills are required.
Computer Science (8) Mathematics (25)

 About the Project