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Applied Physics PhD Projects, Programs & Scholarships in Manchester

We have 22 Applied Physics PhD Projects, Programs & Scholarships in Manchester

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Showing 1 to 10 of 22
  Combining Synthetic Biology and Modelling to Understand and Predict Evolution
  Dr M Lagator, Dr J-M Schwartz
Applications accepted all year round

Funding Type

PhD Type

Mutations, and their effects on organismal fitness, lie at the heart of understanding and predicting evolution. Because of its central role in determining evolutionary outcomes, much experimental and theoretical effort has been put into describing the fitness effects of mutations.
  Modelling of cleaning and decontamination problems on porous and absorbent substrates
  Dr J Landel
Applications accepted all year round

Funding Type

PhD Type

Cleaning and decontamination processes of surfaces can rely on different mechanisms to remove an unwanted patch of substance attached to a substrate.
  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.
  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.
  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.
  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.
  Zero-Shot Learning and Applications
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

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.
  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.
  Evolving novel and unconventional methods for making precise angstrom capillaries using 2D materials, and exploring molecular separation through them
  Dr R Boya
Applications accepted all year round

Funding Type

PhD Type

Angstrom-scale pores and capillaries have been studied intensively because of their importance in many natural phenomena and use in numerous applications.
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