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Home > Research > Machine Learning > Machine Learning PhD Opportunities

Machine Learning PhD Opportunities

Doing your PhD in the NICTA Machine Learning Group affords a number of benefits:

NICTA scholarship top-up
NICTA support for academic travel
access to world-class machine learning researchers

Please feel free to contact any of the researchers below for more information on PhD opportunities with them, please include:

your CV listing any degrees, awards, prior publications, and work experience
your academic transcripts for all previous coursework
your area(s) of interest for a PhD
a list of three references who may be contacted

Supervisor Name	Where to Apply	PhD Supervision Areas
William Billingsley	U. of Queensland	Human-AI interaction Technology enhanced education Integrating disparate analysis Visual language and end-user programming
Edwin Bonilla	ANU	Non-parametric Bayesian methods Gaussian processes Efficient inference in Large-scale probabilistic models Bayesian methods for active learning of user preferences and recommender systems Applications of probabilistic models in computer vision, document analysis and dynamical systems
Wray Buntine	ANU	Graphical models Bayesian inference Topic modeling Document analysis
Tiberio Caetano	ANU	Graphical models Structured prediction Social networks
Lawrence Cavedon	U. of Melbourne	Text mining for biomedical and clinical applications
Fang Chen	UNSW U. of Sydney	Human-computer interfaces Cognitive load management
Eric Choi	UNSW U. of Sydney	Multimodal input processing and fusion Cognitive user modeling Adaptive decision support Speech paralinguistic processing
Stephen Gould	ANU	Machine learning for computer vision
Marcus Hutter	ANU	Reinforcement learning Algorithmic information theory Bayesian statistics Model selection
Chris Leckie	U. of Melbourne	Clustering Graph mining Scalable data mining Applications in network intrusion detection Wireless sensor networks and bioinformatics
Mark Reid	ANU	Learning theory
Scott Sanner	ANU	Structured probabilistic inference/graphical models Sequential decison-making Social media recommendation Statistical relational learning
Hanna Suominen	ANU U. of Canberra	Machine learning Mathematical modeling and human language technologies related to health and wellness
William Uther	UNSW	Sequence prediction Sequential decision making Traffic modelling
Christfried Webers	ANU	Differential geometry Numerical linear algebra Optimisation on Manifolds
Bob Williamson	ANU	Learning theory

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