PhD Fellowship in Real-Time IoT Analytics at Edge
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.
In this project, we will develop an edge analytics framework for real-time IoT data analytics to address the limitations of existing data-center-based analytics:
(1) We will study existing software platforms for data analytics so as to: (i) examine what extent they support real-time IoT data analytics at Edge, and (ii) understand their performance tradeoffs and deficiencies.
(2) We will develop tailor-made techniques for real-time IoT stream data analytics at Edge, leveraging our previous expertise in data engineering.
(3) We will study the relationship between sensor data representation, data storage architecture, and data analytics, to understand their impact on latency, accuracy, scalability, and fault tolerance.
(4) We will optimize the sensor data representation and compression not only for transmission but also for facilitating and accelerating data analytics.
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), although exceptional candidates from related disciplines (e.g., Applied Mathematics) will also be considered.
- 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.
Place of employment and place of work:
The place of employment is Aarhus University, and the place of work is Department of Electrical and Computer Engineering, Åbogade 34, DK-8200 Aarhus N., Denmark.
Applicants seeking further information are invited to contact:
How to apply:
Please follow this link to submit your application. Application deadline is 15 November 2021 at 23:59 CET. Preferred starting date is 1 February 2022.
For information about application requirements and mandatory attachments, please see our application guide.
Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.
All interested candidates are encouraged to apply, regardless of their personal background. Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants. Salary and terms of employment are in accordance with applicable collective agreement.
- Offered SalaryNot Specified
- Career LevelNot Specified
- ExperienceNot Specified
- QualificationMaster's Degree(M.Sc.)
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