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Northumbria University, Faculty of Engineering and Environment PhD Projects, Programs & Scholarships

We have 46 Northumbria University, Faculty of Engineering and Environment PhD Projects, Programs & Scholarships

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  A combined framework of machine learning and full waveform inversion for electromagnetic sensing of infrastructure (Advert Reference: SF19/EE/MCE/WARREN)
  Dr C Warren, Prof J Martin
Applications accepted all year round

Funding Type

PhD Type

Electromagnetic sensing systems, such as Ground Penetrating Radar (GPR), are critical tools to allow us to evaluate the condition of surface and sub-surface infrastructure.
  A Novel Combination of Game Theoretic Strategies for Efficient Energy Management of Home-Microgrids (SF19/EE/MPEE/MARZBAND)
  Dr M Marzband, Dr M Jovanovic
Applications accepted all year round

Funding Type

PhD Type

Home-Microgrids (H-MGs) or smart green buildings are considered as a credible alternative to conventional buildings, as they are very efficient and environmentally friendly.
  A Novel Method to Recycle Composite Machining Swarf (Advert Reference: SF19/EE/MCE/SHYHA)
  Dr I Shyha, Prof K Lafdi
Applications accepted all year round

Funding Type

PhD Type

The use of composite materials, in particular polymeric based, is considerably rising in many industries including aerospace, automotive, sports and medical equipment.
  An investigation of the impact of disruption on occupiers and landlords in Commercial Real Estate Markets (Advert Ref: SF19/EE/ABE/GREENHALGH)
  Prof P Greenhalgh, Dr K Muldoon-Smith
Applications accepted all year round

Funding Type

PhD Type

Commercial real estate markets are increasingly prone to dynamic and disruptive change, the drivers of which include technological, social, environmental and employment practices.
  Architectural Re-Use and the Mega Event Stadium (Advert Reference: SF19/EE/ABE/BROWN)
  Ms L Brown, Prof R D Giddings
Applications accepted all year round

Funding Type

PhD Type

Sport mega events are best understood as large scale cultural events which have dramatic character, mass popular appeal, international significance, and the power to deliver long-lasting benefits in cities.
  Architectural Reuse and the Olympic Athletes Village (Application Ref: SF19/EE/ABE/SEO)
  Dr KW Seo, Ms L Brown
Applications accepted all year round

Funding Type

PhD Type

The 2012 Olympic Village in London, UK, was constructed for the 2012 Summer Olympic Games, as part of a wider masterplan to revitalise an industrial area of East London and deliver high quality, mixed use, affordable housing, and sustainable communities.
  Automated human detection in crowded scenes using fused machine learning systems (Application Ref: SF19/EE/CIS/KHELIFI)
  Dr F Khelifi, Dr A Belatreche
Applications accepted all year round

Funding Type

PhD Type

Automated human detection in dense crowds has recently emerged as one of the key AI elements to address many problems in computer vision such as scene analysis, suspicious behaviour/fight detection and human tracking.
  BioVision: bio-inspired computer vision using deep architectures (Advert Reference: SF19/EE/CIS/BELATRECHE)
  Dr A Belatreche, Dr F Khelifi
Applications accepted all year round

Funding Type

PhD Type

The primate visual system outperforms the existing automated vision systems in almost all measures.
  Comparative Studies of Grid-Connected Doubly-Fed Wind Generators (Advert Reference: SF19/EE/MPEE/JOVANOVIC)
  Dr M Jovanovic, Prof K Busawon
Applications accepted all year round

Funding Type

PhD Type

The Doubly-Fed Induction Generator (DFIG) has been traditionally used for wind turbines with limited speed ranges due to its good performance and a low cost partially-rated converter.
  Complex network analysis and resilience in large-scale multi-layer networks (Application Ref: SF19/EE/CIS/SHANG)
  Dr Y Shang, Prof A Bouridane
Applications accepted all year round

Funding Type

PhD Type

The goal of this project is to investigate and develop new data science approaches to model large-scale networks.
  Computer Vision with Deep Learning and Multi-Drones (Application Ref: SF19/EE/CIS/SHUM)
  Dr H Shum
Applications accepted all year round

Funding Type

PhD Type

We are seeking a motivated person interested in a self-funded doctoral study PhD degree. With the training provided, the PhD will research, implement solutions, and produce research papers in the exciting areas of computer vision with deep learning and multiple drones.
  Data analysis for digital-entrepreneurship/self-employment (Application Ref: SF19/EE/CIS/ANWAR)
  Dr MN Anwar, Dr A Ali
Applications accepted all year round

Funding Type

PhD Type

This age of big data gives a great opportunity to turn large-size datasets with high-velocity and diverse structures into real advantage in many fields, such as health, economics, education and disaster prevention.
  Deep Learning based Network Forensic Methods for Investigating Botnets in the IoT (Application Ref: SF19/EE/CIS/ASLAM)
  Dr N Aslam, Dr B Issac
Applications accepted all year round

Funding Type

PhD Type

The process of designing IoT protocols and sensors and the lack of standards are the main reasons why the IoT is an easy target for botnets.
  Design and development of paper/responsive polymer hybrid micro-device (Advert Reference: SF19/EE/MCE/XU)
  Dr B Xu, Dr Y Li
Applications accepted all year round

Funding Type

PhD Type

Paper micro-device is a technology that is particularly well-suited for use in public health, diagnostics for resource-limited settings, veterinary medicine, testing of food and water quality, and environmental monitoring.
  Developing a holistic data analytical model for reducing workload on doctors in NHS General Practices by safely shifting significant workload to other competent professionals (Advert Reference: SF19/EE/CIS/ALI)
  Dr A Ali, Dr S Khan
Applications accepted all year round

Funding Type

PhD Type

Primary care is critical to the effectiveness and sustainability of the health system in the UK. This is recognised in the current transformation of healthcare that aims to provide care and treatment in the community rather than through hospital-centric services.
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