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Machine learning and computer vision for data organisation and exploration

Country/Region : UK - United Kingdom

Website : http://www.bath.ac.uk/science/graduate-s...

Description

Machine learning and computer vision techniques attempt to classify and order data according to objective measures but how might users browse data when these criteria are subjective and straddle classification boundaries?
The crux of the problem is to understand how these criteria can be formed, communicated and generalised without having to manually describe and quantify them. We aim to form this knowledge into new machine learning and computer vision algorithms. During the PhD, students will develop algorithms on deep learning, graphical inference, data embedding and transfer learning. Students will also learn how to analyse and work with image, depth, and geometry data.
The successful candidate/s will join the Visual Computing Group (http://www.bath.ac.uk/comp-sci/research/visual-com...) at University of Bath’s Computer Science Department, a vibrant research team with six academics and a group of PhD students and post-doctoral researchers. The student/s will be supervised by Dr. Kwang In Kim (http://www.bath.ac.uk/comp-sci/contacts/academics/...).
Requirements: Applicants should have a first class or good upper second in Computer Science, Mathematics, Statistics, or closely related field. Applicants also need to have a strong background in mathematics and high proficiency in Matlab, C, C++, or Java. Having experienced with an MSc project in machine learning, vision, or any related areas is a plus but is not required.
Application Instructions: Applicants are invited to send their CV to Dr Kwang In Kim (k.kim at bath.ac.uk) so that he can help them preparing the formal application. If they meet the minimum criteria, they will be asked to submit a formal application via the Postgraduate Admissions Portal:
http://www.bath.ac.uk/science/graduate-school/rese...
Applications may close earlier than the advertised deadline if a suitable candidate is found; therefore, early application is strongly recommended.
Anticipated start date: 2 October 2017.

Last modified: 2016-12-15 11:34:27