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  Multi-task Learning and Applications - developing novel learning systems and learning algorithms for multi-task learning
  Dr K Chen
Applications accepted all year round
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.
  Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning
  Dr K Chen
Applications accepted all year round
Traditionally there are two main paradigms in machine learning, supervised vs. unsupervised learning.
  Zero-Shot Learning and Applications - investigating effective zero-shot learning techniques as fundamental research and their applications in real world problems
  Dr K Chen
Applications accepted all year round
Zero-shot learning refers to a novel paradigm on learning how to recognise new concepts by just having a description of them. For example, zero-shot learning works on a setting of solving a classification problem when no labelled training examples are available for all classes, which are divided into two class subsets.
  PhD Industrial CASE studentship - Deep Learning for Face Image Analytics - Development of strategies for extracting facial attribute knowledge from deep learning architectures on image data
  Dr J Bacardit
Applications accepted all year round
Number of awards. 1. Start date and duration. Academic year 2018/2019 for 4 years. Application closing date. We would like to keep this advert open until a suitable candidate has been identified.
  Social Learning in Artificial Evolutionary Systems
  Dr J Borg
Applications accepted all year round
Artificial evolutionary systems, be they simulated or grounded in physical robots, provide a novel and cutting-edge way to investigate the emergence and evolution of adaptive and intelligent behaviours.
  Deep Learning Driven Intelligent Internet of Things (RDF19/EE/CIS/WEI)
  Prof B Wei
Application Deadline: 25 January 2019
Internet of Things (IoT) have been widely applied in many fields, such as smart homes/cities and industry monitoring, as an indispensable enabler to the realisation of Industry 4.0.
  Deep Learning for Temporal Information Processing
  Dr K Chen
Applications accepted all year round
Temporal information process covers a broad class of learning problems where knowledge can be acquired from data of a sequential order, e.g.
  Self-Learning Intrusion Detection System Through Machine Learning (Advert Reference: SF18/CIS/ISSAC)
  Dr B Isaac
Applications accepted all year round
As network applications are increasingly used with the popularity of broadband networks and Internet of Things (IoT), network security is a very important issue.
  Distance learning research with the College of Arts and Law
Distance learning, otherwise known as online learning, is a flexible approach to the delivery of our postgraduate research courses.
  (Turing) Can we use machine learning risk prediction models in diverse settings in the healthcare system?
  Prof T Van Staa, Dr N Geifman, Dr V Palin
Application Deadline: 31 January 2019
There is considerable potential to use routinely collected electronic health records (EHRs) to estimate the risks of major clinical outcomes (such as heart attack, mortality or hospital admission) which could then be used by clinicians to target treatment.
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