Artificial Intelligence (hazards) PhD Projects, Programmes & Scholarships for Self-funded Students

We have 7 Artificial Intelligence (hazards) PhD Projects, Programmes & Scholarships for Self-funded Students

Application of Hyper-Spectral Imaging to Assess the Level of Aflatoxins in Pistachio Nut

Leeds Beckett University School of Built Environment, Engineering and Computing

The School of Built Environment, Engineering and Computing at Leeds Beckett University is currently accepting applications for up to 4 funded full-time PhD Research Studentships. Read more

Supervisor: Dr A Sheikh-Akbari

17 May 2024 PhD Research Project Funded PhD Project (Students Worldwide)

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3D modelling of subsea jet trenching and its sediment plume impacts

University of Liverpool School of Engineering

The EPSRC Centre for Doctoral Training in Net Zero Maritime Energy solutions (N0MES) has a 4-year funded PhD place available for an exceptional researcher. Read more

Supervisors: Dr YZ Zhou, Dr X Zhang

20 May 2024 PhD Research Project Funded PhD Project (Students Worldwide)

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Integration of Internet of Things (IoT) and Artificial Intelligence (AI) for Flame Monitoring and Safety Enhancement in Industrial Settings

Canterbury Christ Church University SCHOOL OF ENGINEERING, TECHNOLOGY AND DESIGN

This research project presents an exceptional opportunity for individuals interested in harnessing the power of Internet of Things (IoT) and Artificial Intelligence (AI) to enhance flame monitoring and safety protocols in industrial environments. Read more

Supervisor: Dr H Hassanin

Year round applications PhD Research Project Self-Funded PhD Students Only

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Multi Risk and Resilience

Cardiff University Cardiff School of Engineering

Self funded 3 year PhD, October 2024 start. This interdisciplinary project aims to enhance infrastructure resilience and sustainability using advanced numerical modelling combined with machine learning. Read more

Supervisor: Dr VN Novelli

31 May 2024 PhD Research Project Self-Funded PhD Students Only

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Virtual Reality and Safety Training: an evolutionary approach to enhancing hazard perception in cycling communities

Anglia Ruskin University ARU School of Engineering and the Built Environment

Proposed supervisory team. Dr Shabnam Sadeghi Esfahlani. Dr Matthew A. Timmis. Dr Kjell van Paridon. Theme. AI, Robotics and Automation. Impact themes. Read more

Supervisor: Dr S Sadeghi-Esfahlani

Year round applications PhD Research Project Self-Funded PhD Students Only

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A Unified framework for early anomalies detection

University of Salford School of Science, Engineering and Environment

Information on this PhD research area can be found further down this page under the details about the Widening Participation Scholarship given immediately below. Read more

Supervisor: Dr S ur Rehman

Year round applications PhD Research Project Self-Funded PhD Students Only

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A computer vision model to detect black mold

University of Salford School of Science, Engineering and Environment

Information on this PhD research area can be found further down this page under the details about the Widening Participation Scholarship given immediately below. Read more

Supervisors: Dr T Mansouri, Dr A Alameer

Year round applications PhD Research Project Self-Funded PhD Students Only

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We have 7 Artificial Intelligence (hazards) PhD Projects, Programmes & Scholarships for Self-funded Students

Artificial Intelligence (hazards) PhD Projects, Programmes & Scholarships for Self-funded Students

We have 7 Artificial Intelligence (hazards) PhD Projects, Programmes & Scholarships for Self-funded Students

Application of Hyper-Spectral Imaging to Assess the Level of Aflatoxins in Pistachio Nut

3D modelling of subsea jet trenching and its sediment plume impacts

Integration of Internet of Things (IoT) and Artificial Intelligence (AI) for Flame Monitoring and Safety Enhancement in Industrial Settings

Multi Risk and Resilience

Virtual Reality and Safety Training: an evolutionary approach to enhancing hazard perception in cycling communities

A Unified framework for early anomalies detection

A computer vision model to detect black mold

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