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PhD Position with Scholarship at the Weka Machine Learning group at University of Waikato, New Zealand

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PhD Position with Scholarship at the Weka Machine Learning group at University of Waikato, New Zealand
от Роман Станиславович Самарев - Воскресенье, 9 Июнь 2019, 09:33

LOCATION: WEKA Machine Learning Group at University of Waikato


 New systems with novel mining techniques are necessary due to the velocity, but
also variety and variability, of high-speed data streams, particularly in IoT
applications. This challenging setting requires algorithms that use an extremely
small amount of time and memory resources, and are able to adapt to changes while
not stopping the learning process. Moreover, they should be distributed to allow
them to run on top of Big Data infrastructures. How to do this accurately in a
fully automatic and transparent elastic, real-time, system is the main challenge
for real-time analytics. In this scenario, high-performing ensemble setups for
machine learning from data streams, such as online bagging, leveraging bagging,
and random forests, are currently state-of-the-art. On the other hand, deep
neural networks are immensely popular and successful in offline settings, owing
in part to the proliferation of interest and oft-advertised successes in deep
learning. These algorithms can learn incrementally, but they have so far proved
too sensitive to hyper-parameter options and initial conditions to be considered
for the IoT data stream setting.

In this context, we are looking for candidates for a PhD funding opportunity in
the area of Machine Learning for Data Streams.

The successful candidate should have solid bases in data management, algorithms
and knowledge of machine learning topics. There is an important application
development part in this project. As such, the students should be at ease with
programming in well-known languages (C++, Java, Python). Evidence of previous
experience in research in data-related subjects (master-level internships,
publications) will be appreciated.

For further details and to apply, please submit your CV, a cover letter, Master
level transcripts, a reference letter (or the coordinates of a person willing to
give one) to this website: