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Statistics (unknown) PhD Projects, Programs & Scholarships

We have 5 Statistics (unknown) PhD Projects, Programs & Scholarships

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  Probabilistic Graphical Models in Multi-Target Tracking
  Dr J Hopgood, Prof B Mulgrew, Dr Y Altmann
Application Deadline: 30 June 2019

Funding Type

PhD Type

This PhD is in the area of Statistical Signal Processing and Information Fusion, and is concerned with Joint Sensor Calibration and Hyperparameter Estimation in Multi-Target Tracking using Message Passing Algorithms.
  A Theoretical Investigation in Dynamic Pricing and Revenue Management
  Research Group: Operational Research
  Dr A Avramidis
Applications accepted all year round

Funding Type

PhD Type

The project is aimed at a primarily theoretical investigation in Dynamic Pricing and Revenue Management, drawing from Applied Probability and Mathematical Statistics, and contributing to these areas.
  Can the hierarchical behaviour of dairy cows tell us about their experience of historical treatment for disease, and what can we tell from their behaviours about future response to disease?
  Dr A Butterworth
Application Deadline: 3 June 2019

Funding Type

PhD Type

The project. Animal health can present economic and animal welfare concerns for dairy farmers.
  Translation of Cardiovascular Control Models: Are ‘resistant hypertension’ patients truly drug resistant and does neuronal dysregulation contribute?
  Dr R Barrett-Jolley, Prof GYH Lip, Dr A Shantsila
Applications accepted all year round

Funding Type

PhD Type

Hypertension is major health problem across the world affecting 30-40% of adults, and its prevalence continues to grow, especially in developing countries.
  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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