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University of Manchester Applied Physics PhD Projects, Programs & Scholarships

We have 20 University of Manchester Applied Physics PhD Projects, Programs & Scholarships

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  EPSRC Centre for Doctoral Training in Advanced Metallic Systems
University of Sheffield, Doctoral Training in Materials Engineering for Engineering and Physical Sciences Graduates With the move to a stainable economy, new materials are essential to the next generation of technologies required by UK industry for demanding applications across all sectors - from manufacturing to healthcare, aerospace and energy.
  EPSRC Centre for Doctoral Training in Power Networks
 . Power Networks are at the heart of any approach to decarbonise the energy system. The future Power Network will need to support.
  PhD Opportunities in fusion
Fusion has the potential to meet our energy needs in a sustainable way, with minimum impact on the environment.
  (BBSRC DTP) Sharpening up protein NMR: ultra-high resolution experiments
  Prof M Nilsson, Dr A Golovanov, Prof GA Morris
Application Deadline: 31 January 2019
The project directly addresses the topic of technology development (i.e. new NMR experiments), as well as providing advanced training in the skills required to operate the latest NMR spectrometers at an expert level and to apply computational techniques to biological data.
  Automatic Emotion Detection, Analysis and Recognition.
  Dr K Chen
Applications accepted all year round
Affective computing is a branch of Artificial Intelligence that relates to, arises from, or deliberately influences emotion and other affective phenomena.
  Contextualised Multimedia Information Retrieval via Representation Learning
  Dr K Chen
Applications accepted all year round
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
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
Traditionally there are two main paradigms in machine learning, supervised vs. unsupervised learning.
  Machine Learning and Cognitive Modelling Applied to Video Games
  Dr K Chen
Applications accepted all year round
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 - 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.
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