FindA University Ltd Featured PhD Programmes
Engineering and Physical Sciences Research Council Featured PhD Programmes
FindA University Ltd Featured PhD Programmes
University College London Featured PhD Programmes
FindA University Ltd Featured PhD Programmes
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Materials Science (ai) PhD Projects, Programs & Scholarships

We have 18 Materials Science (ai) PhD Projects, Programs & Scholarships

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  Artificial Intelligence Methods - Automated Defect Recognition of Ultrasonic Data in NDT
  Prof P Wilcox, Prof B Drinkwater
Applications accepted all year round

Funding Type

PhD Type

Applications are invited for a research studentship in the field of Machine Learning / Artificial Intelligence (AI) in non-destructive evaluation (NDE) techniques leading to the award of an Engineering Doctorate (EngD) degree.
  Developing AI approach to autonomous dismantling/packaging of nuclear installations
  Dr X Jia, Prof Shane Xie
Applications accepted all year round

Funding Type

PhD Type

At some stage in the decommissioning of nuclear installations (e.g., reactors and gloveboxes), it is inevitable that large metal structures (e.g., reactor vessels, gloveboxes, structural components) need to be cut into smaller pieces to be packed in containers, for temporary storage or permanent disposal or simply for transportation.
  Smart and Soft Sensors: developing new sensing technologies for application in space
  Prof S Nefti-Meziani
Applications accepted all year round

Funding Type

PhD Type

Supervisors. Prof. Samia Nefti-Meziani [email protected] 0161 295 4540, Dr Steve Davis. The Centre for Robotics and Autonomous System at the University of Salford is one of the largest Robotics groups in the UK.
  The application of soft robotics for the next generation of robot used for space exploration and astronaut assistance
  Prof S Nefti-Meziani
Applications accepted all year round

Funding Type

PhD Type

Supervisors. Prof. Samia Nefti-Meziani [email protected] 0161 295 4540, Dr Steve Davis. The Centre for Robotics and Autonomous System at the University of Salford is one of the largest Robotics groups in the UK.
  Artificial Intelligence & Nanophotonic Materials
  Prof K MacDonald, Prof N Zheludev, Dr V Savinov
Applications accepted all year round

Funding Type

PhD Type

Artificial intelligence (AI) is transforming the way in which scientific discoveries are made. In this project we will apply AI to the development of advanced materials for photonic applications.
  BCAST PhD Studentship: Advanced models to simulate water quench related distortions in aluminum extrusions used for battery enclosures
  Prof I Chang
Application Deadline: 31 August 2019

Funding Type

PhD Type

Applications are invited for ONE full-time EPSRC Industrial CASE (iCASE) PhD studentship for the project above at Brunel Centre for Advanced Solidification Technology (BCAST), Brunel University London starting from 1st October 2019.
  EPSRC CDT in Nuclear Energy - GREEN (Growing skills for Reliable Economic Energy from Nuclear)

Funding Type

PhD Type

The GREEN CDT is a consortium of the five universities of Lancaster, Leeds, Liverpool, Manchester and Sheffield, which aims to train the next generation of expert nuclear scientists and engineers.
  PhD Studentship in Nano-electronics: Resistive-switching memory devices (ReRAM/FeRAM/memristor) and its application in neuromorphic and in-memory computing systems
  Prof W Zhang
Applications accepted all year round

Funding Type

PhD Type

Applications are invited for a PhD position in the Department of Electronics and Electrical Engineering, Liverpool John Moores University (LJMU).
  EPSRC CDT in Nuclear Energy - GREEN (Growing skills for Reliable Economic Energy from Nuclear)

Funding Type

PhD Type

The GREEN CDT is a consortium of the five universities of Lancaster, Leeds, Liverpool, Manchester and Sheffield, which aims to train the next generation of expert nuclear scientists and engineers.
  Computational materials design and discovery
  Prof M J Rosseinsky
Application Deadline: 30 June 2019

Funding Type

PhD Type

This project will suit applicants with interests in the development and application of new methods for the computational understanding and prediction of new materials.
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