Stream
Analytics Research in Big Data
Stream
Analytics is an emerging area of research in Big Data that processes and
analyses ingested events in real-time by comparing or combining
multiple streams with historical data and models. Such analysis includes
detection of abnormalities in data, transforming or enriching of
incoming stream data, and displaying of this real-time data in
dashboard.
A
fully funded PhD position is available under the Data Science Research
Group (DSRG) at the Auckland University of Technology (AUT), Auckland,
New Zealand. The DSRG is conducting research in the areas of Large-Scale
Data Management (LSDM), Data Mining with an emphasis on stream mining,
Statistical Machine Learning (SML), and Information Visualization (IV).
The research goal is to develop new frameworks and algorithms that
process and analyse large volume of stream data and predict useful
trends. This project will investigate the area of stream mining and
analytics by focusing on topics of frequent item set mining with respect
to master/historical data.
REQUIREMENTS:
The requirements for this position are as following:
- The candidate should have completed his/her research-based Master in Computer Science or related discipline from well-reputed university with high grades.
- The candidate should have proven background in Databases, Data Mining, and Big Data.
- The candidate must be competent in programming e.g. Java, Python, and MySQL databases.
- The candidate should have strong skills in academic research writing.
- Knowledge of stream processing and analytics is a plus point.
- A record of good publications is a plus point.
- The selected candidate will be expected to publish in top ranking journals and conferences.
SCHOLARSHIP:
- The scholarship is for both national and international full time students.
- The scholarship is for three years covering the tuition fees and the annual stipend of NZD 25,000.
APPLICATION
Interested
candidates should email their CV, copy of their Master transcript and
thesis, and PhD proposal to Dr. Muhammad Asif Naeem at mnaeem@aut.ac.nz.
The webpage is
https://iwdm.aut.ac.nz/asif/For more Scholarships
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