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Statistics (knowledge) PhD Projects, Programs & Scholarships

We have 62 Statistics (knowledge) PhD Projects, Programs & Scholarships

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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

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.
  Studentship Opportunity in Deterioration relationships for a variety of water main materials
  Dr D Jesson, Dr M Mulheron
Applications accepted all year round

Funding Type

PhD Type

For something that we drink every day, water is a surprising problem for the materials scientist. there are no materials suitable for large scale water distribution networks that remain unaffected by their contents over time.
  Optimising the nanoscale behaviour of novel materials for energy applications
  Dr S Christopoulos
Application Deadline: 28 June 2019

Funding Type

PhD Type

Coventry University (CU) is inviting applications from suitably-qualified graduates for a fully-funded PhD studentship. The preparation of advanced material compositions has led to a wide range of applications.
  PhD Student (f/m/d) Grid Cell Coding in Old Age
  Prof T Wolbers
Applications accepted all year round

Funding Type

PhD Type

This position will be based in the Aging & Cognition Research group (www.wolberslab.net), which seeks (i) to understand fundamental mechanisms of cognitive aging under naturalistic conditions, (ii) to establish novel clinical assessment tools, and (iii) to develop interventions that help maintain people’s independence.
  EPSRC Centre for Doctoral Training in Cloud Computing for Big Data

Funding Type

PhD Type

This new multi-million pound initiative will train the next generation of experts in the analysis of "big data" using advanced statistical methodologies and the latest cloud computing technologies, addressing an acute worldwide skills shortage.
  SILVER: Improving nuclear decommissioning project performance through reference class forecasting
  Prof N Brookes, Dr G Locatelli
Applications accepted all year round

Funding Type

PhD Type

What is the research area?. This project will focus on ‘reference class forecasting’ which is a technique that has been formulated to improve the estimation of time and cost of in large capital projects, like the nuclear decommissioning projects undertaken at Sellafield.
  Developing Artificial Intelligence Tools for the Identification of Sleep Apnoea
  Dr Y Zheng, Dr S Craig, Prof GYH Lip
Applications accepted all year round

Funding Type

PhD Type

Obstructive Sleep Apnoea (OSA) occurs due to the repetitive closure of the pharynx during sleep, leading to sleep fragmentation and, often, daytime somnolence.
  An intelligent imaging technology for automatic characterisation of the refractive power of human eye
  Prof Y Shen, Dr Y Zheng
Application Deadline: 28 June 2019

Funding Type

PhD Type

The project is supported by the University of Liverpool Doctoral Network in Future Digital Health, which is directed at creating and maintaining a community of AI health care professionals that can realise the benefits that AI can bring to Health Care.
  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.
  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

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.
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