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Information Science (self) PhD Projects, Programs & Scholarships

We have 84 Information Science (self) PhD Projects, Programs & Scholarships

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  Trust management in the Internet of things (Application Ref: SF19/EE/CIS/KHAN)
  Dr S Khan, Dr A Ali
Applications accepted all year round

Funding Type

PhD Type

Internet of things (IoT) is a contemporary technology, which links a tremendous number of devices with each other to ease life via many useful services such as information exchange, monitoring, and control.
  PhD Studentship in Abstract Forward Models for Modern Games
  Dr D Perez-Liebana
Application Deadline: 31 January 2020

Funding Type

PhD Type

Applications are invited for a full PhD Scholarship starting in April 2020 (or as soon as possible thereafter) to undertake research in the areas defined in the EPSRC project “Abstract Forward Models for Modern Games”.
  PhD Studentship in Machine Learning and IoT
  Dr M Jaber
Application Deadline: 14 February 2020

Funding Type

PhD Type

Applications are invited for a full PhD Scholarship starting April 2020 (or as soon as possible thereafter) to undertake research in the area of Machine learning and IoT in the context of Smart Cities.
  Automatic Activity Analysis, Detection and Recognition
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

Activity detection, analysis and recognition are related to several important areas in machine perception research and applications.
  Automatic Music Generation via Deep Learning
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

Music appears as an art form and cultural activity whose medium is sound organized in time, which is an ultimate language for human beings.
  Biologically-Plausible Continual Learning
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

Continual learning (aka lifelong learning) refers to a problem on how a learning system learns multiple tasks in succession over the lifespan where later tasks do not degrade the performance of the system learned for the earlier tasks and, ideally, the system can leverage the knowledge learned in previous tasks to facilitate learning the new tasks better.
  Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

Traditionally there are two main paradigms in machine learning, supervised vs. unsupervised learning.
  Information Component Analysis via Deep Learning
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

As their prominent characteristics, perceptual data often convey the mixing information, which often results in the inadequate performance for a specific perceptual information processing task due to the interference of irrelevant information components.
  Machine Learning and Cognitive Modelling Applied to Video Games
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

Video games have been viewed as an ideal test bed for the study of AI. However, most of the academic work in this area focused on traditional board and card games where limited AI techniques have been tested.
  Multi-task Learning and Applications
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

In traditional machine learning, a learning system can be trained to deal with a specific single task, while human is able to complete multiple tasks with the same learning strategy.
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