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Statistics (electrical) PhD Projects, Programs & Scholarships

We have 16 Statistics (electrical) PhD Projects, Programs & Scholarships

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  Artificial intelligence for smart engines
  Research Group: nCATS
  Dr L. Wang
Application Deadline: 31 August 2020

Funding Type

PhD Type

Project description.
  Aircraft Electrification
  Research Group: Energy Systems
  Dr J Marco, Dr A Barai
Applications accepted all year round

Funding Type

PhD Type

PROJECT OVERVIEW. This fully funded PhD studentship represents a unique opportunity to undertake advanced characterisation, control system design and modelling research in close partnership with Vertical Aerospace (http://www.vertical-aerospace.com).
  Unbiased Estimation for Inverse Problems
  Dr A Jasra
Applications accepted all year round

Funding Type

PhD Type

This project concerns the development of Markov chain Monte Carlo and sequential Monte Carlo methods for unbiased estimation in Bayesian inverse problems.
  Statistical machine learning for efficient and reliable estimation in computationally intensive systems
  Dr X Chen
Application Deadline: 8 March 2020

Funding Type

PhD Type

A computationally intensive Industrial process is sometimes considered and modelled as a black-box system, for which one can only observe/analyse system’ inputs and outputs but with very limited knowledge of its internal mechanism.
  Towards prediction of synthetically accessible organic molecular crystals
  Dr V Kurlin, Prof A Cooper
Application Deadline: 15 March 2020

Funding Type

PhD Type

The vision of the project is to transform materials discovery into a routine computational task. Materials discovery still needs a lot of human expertise, trial-and-error and even pure luck, because there is no rigorous theory to guide an efficient search in the huge space of all theoretically possible materials.
  A Neural Fuzzy Fusion Engine for Human-Machine Autonomous Systems
  Dr YK Wang
Applications accepted all year round

Funding Type

PhD Type

Abstract. This project aims to develop an intelligent engine to adaptively fuse multiple trust-based information from various agents in human machine autonomous systems (HMAS).
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