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The University of Manchester, Department of Computer Science PhD Projects, Programs & Scholarships

We have 18 The University of Manchester, Department of Computer Science PhD Projects, Programs & Scholarships

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  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.
  Cognitive Robotics and Human Robot Interaction
  Prof A Cangelosi
Applications accepted all year round

Funding Type

PhD Type

Research on cognitive robotics focuses on the design of intelligent behaviour and sensorimotor and cognitive skills in robots, by taking inspiration from human cognition (psychology) and brain mechanisms (neuroscience).
  Combining Concolic Testing with Machine Learning to Find Software Vulnerabilities in the Internet of Things
  Dr LC Cordeiro
Applications accepted all year round

Funding Type

PhD Type

Concolic testing is a software verification technique that has been successfully applied to find subtle bugs in embedded software.
  Component-based Software Development.
  Dr K Lau
Applications accepted all year round

Funding Type

PhD Type

In Software Engineering, component-based development (CBD) aims at reducing production cost and time-to-market by reusing pre-existing components from a repository.
  Contextualised Multimedia Information Retrieval via Representation Learning
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

Multimedia Information Retrieval (MIR) is an important research area in AI that aims at extracting semantic information from multimedia data sources including perceivable media such as audio, image and video, indirectly perceivable sources such as text, bio-signals as well as not perceivable sources such as bio-information, stock prices, etc.
  Deep Learning for Temporal Information Processing
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

Temporal information process covers a broad class of learning problems where knowledge can be acquired from data of a sequential order, e.g.
  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.
  Formal Methods: Hybrid Event-B and Rodin
  Dr R Banach
Applications accepted all year round

Funding Type

PhD Type

These days, the world is increasingly being populated with systems that interact directly with the physical world. Of course such systems have existed for a long time in niche safety-critical areas, eg.
  Formal Semantics of the Perfect Language
  Dr R Banach
Applications accepted all year round

Funding Type

PhD Type

Perfect Developer, from Escher Technologies, is a language system that combines specification constructs, programming constructs, code generation and verification.
  Guaranteeing Reliability for IoT Edge Computing Systems
  Dr V Pavlidis
Applications accepted all year round

Funding Type

PhD Type

pical IoT Edge devices collect data from several sensors and may also control several actuators depending on the target application.
  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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