Postgraduate research opportunities in the School of Science, Engineering and Environment

The techniques of Control Theory, Artificial Intelligence and Statistical Data Analysis have been applied to engineering systems with great success, and the resulting is better energy efficiency using storage. There are multiple research PhD projects in this field and in particular forecasting energy demand and mathematical modelling for control of energy storage. In this context there are three projects proposed below and candidates can apply to one of the research interest:

• Price forecasting for control of energy storage
• Aggregation effect on storage control
• Analysing the fractal properties of energy demand series and practical implications for storage control algorithms

Find out more and apply for this project:

Advanced Modelling of Energy Demand and Carbon Reduction for Optimised Control and Management of Energy Storage Systems

Metaverse airport navigation is an innovative use of virtual reality (VR) and augmented reality (AR) to enhance passenger experience. This could be a tool for helping passengers navigate large, complex airports, providing real-time assistance and guidance. With the help of VR headsets, passengers could “walk” through a 3D virtual version of the airport, understanding where they need to go and how long it will take to get there. 

Metaverse airport destination sightseeing is another exciting and immersive concept where passengers can explore a virtual version of their destination before their flight. It combines the idea of tourism with the metaverse, allowing passengers to experience a city or country without leaving the airport, or to explore their next stop from a virtual perspective before traveling there. 

As virtual and augmented reality technologies improve, these experiences could become more lifelike, interactive, and even multi-sensory (e.g., involving taste, smell, or haptic feedback). 

The final scope, aims and focus of the postgraduate research will be discussed with an applicant to consider his / her education, professional experience, career aspirations and interests.

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AR/VR and Metaverse at Airports: Navigation Assistance and Sightseeing Experience

The field of computer vision has witnessed remarkable advancements, fuelled by the development of large-scale foundation models. These models, capable of processing and understanding multiple modalities such as text and images, have opened up new possibilities for a wide range of applications. One particularly promising area is the identification of small and not well-defined objects, which has significant implications for fields like medical imaging, healthcare, and remote sensing.

Objectives:

• Design and optimize a multi-modal vision model that integrates both visual and textual data to enhance the detection of small, ambiguous objects in medical images. 
• Develop a robust, end-to-end computer vision pipeline tailored for remote sensing applications. Our goal is to accurately identify subtle features such as minor infrastructural elements and environmental changes, reducing false positives by approximately 15%. 
• Assess and enhance the generalisability of large-scale foundation models across diverse domains, ranging from medical imaging to remote sensing. We aim to achieve an average detection performance improvement of 15% on multiple benchmark datasets by systematically adapting and refining these models for different applications. 

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Applying Multimodal Foundation Models to Identify Small and Not Well-Defined Objects

Robots as well as Autonomous Vehicle are always very challenging to control due to external forces acting on them and unpredicted or unseen environment. These forces are associated with disturbances which make Robots and Autonomous Vehicle difficult to control. In unseen environment the Robots and Autonomous Vehicle are associated with maps. The purpose of this research project is to use AI (Artificial Intelligence) methods such as reinforcement learning algorithm and control systems based vision. The PhD project will also look at developing techniques based on Deep learning, mathematical modelling, Control theory and Machine learning, and compare with Q-learning, hindsight replay; Neural Network autonomous landing combine with learning environment is a potential avenue to the research.

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Artificial Intelligence and Deep Learning in Robotics and Autonomous Systems

According to the Airport Council International, given the logistical complexities present in an airport environment, AVs have created a myriad of opportunities to automate tasks. These tasks fall within robotics, self-driving vehicles, airport parking services and airside operations. Examples of current and future AV applications at airports include robotic security guards, self-driving wheelchairs, robotics cleaners, robotic trolley collection, robo-taxis, airside passenger bussing, autonomous dollies, foreign object detection, automated runway inspection, robotic runway and apron cleaner, baggage robots, automated baggage loader, autonomous snowplough, robotic wayfinding, autonomous toilet truck, driverless crew bus, autonomous aircraft tugs, autonomous catering truck, etc. 

The proposed research will assess the feasibility of a particular AV implementation for particular airport and its needs, design and implement a proof of concept, and evaluate results.

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Autonomous Vehicles at Airports

The rapid deployment of Large Language Models (LLMs) in various industries—such as healthcare, finance, and education—has highlighted concerns around bias, fairness, and ethical implications. While LLMs have the potential to transform these sectors, their use often results in biased outcomes due to the data they are trained on. This PhD project proposes the development of a comprehensive framework for testing, identifying, and correcting bias in LLMs, with context-specific metrics and test scenarios.

Research Objectives:

• Develop algorithms and techniques to systematically detect bias in LLMs, focusing on sector-specific applications.
• Create methodologies to mitigate identified biases without degrading the performance of the LLMs.
• Test the framework in real-world scenarios within different sectors and validate its effectiveness.

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Bias Testing and Correction in Large Language Models

Virtual representation of an airport within the metaverse, accessible through augmented reality (AR) and virtual reality (VR) technologies, allows passengers to navigate, access information, and interact with the airport environment digitally, often providing features like virtual check-in counters, wayfinding assistance, and even virtual shopping experiences, all while being overlaid on their real-world view through AR or fully immersed in a virtual environment through VR.

To make the virtual tour effective and realistic, the role of Image Processing (IP), Computer Vision (CV) and Deep Learning (DL) are important. For example, the combination of IP, CV, and DL can assist the user in finding an exact item in the shop without confusion, which saves lots of time. Similarly, the check-in counter of a particular airline and gates can be identified. Therefore, this study explores the concepts of inter-disciplinaries to develop a metaverse system that impacts live and real-time applications significantly.

The aim of this research is to develop an airport metaverse through Artificial Intelligence techniques which include IP, CV and DL to provide support to specific categories of passengers, including passengers with physical and hidden disabilities.

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Computer Vision and Deep Learning based AR/VR and Metaverse in Air Transport

Metaverse for advanced air mobility is an innovative concept that blends both virtual and physical environments to facilitate the rise of advanced air mobility (AAM), both urban and rural. These technologies are part of a broader vision of transportation and infrastructure.

Vertiports are dedicated facilities designed for the landing, take-off, and maintenance of vertical take-off and landing (VTOL) air taxis. As advanced air mobility grows, cities will need to integrate vertiports into existing infrastructure, and metaverse vertiports could assist in several ways, from planning and design to passenger experience.

This programme will explore the ways metaverse vertiports can assist the future and the benefits, challenges and potential that come with it.

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Metaverse for Advanced Air Mobility and Vertiports

The project investigates aural diversity by focusing on the anatomical variations of the human ear and their impact on hearing. A state-of-the-art fluid-structural model of the ear will be developed, integrating Computational Fluid Dynamics (CFD) and Finite Element (FE) techniques to simulate sound wave propagation through its inner structures. This model will include the external ear, tympanic membrane, ossicles, and cochlea, providing a comprehensive representation of sound mechanics.

By incorporating minor tissue property variations, this project aims to elucidate how individual differences in anatomy contribute to diverse auditory experiences. The insights gained will be instrumental in guiding the design of next-generation hearing aids tailored to accommodate a wider range of hearing profiles, thereby enhancing auditory healthcare and accessibility.

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Modelling Aural Diversity: A Multiphysics Computational Approach

Hydrogen is considered a promising clean and sustainable energy carrier that could help address global energy and environmental challenges. Hydrogen technology can hold the key to decarbonizing the transportation and the future aviation industry. However, using hydrogen technology in transportation applications is still under development and facing many challenges. The conventional hydrogen storage method via compression or liquefaction can achieve high storage density compared to other storage methods. However, this storage method has some limitations like high pressure needed, high energy cost and safety issues. Overall, improvements are needed in materials, efficiency, and safety for viable hydrogen transportation.

This research aims to rethink the design of hydrogen storage thank by employing multidisciplinary optimisation approach. The multidisciplinary design optimization of hydrogen storage tank integrates CFD simulation with finite element analysis (FEA) to achieve highest possible pressure with minimum stress. This approach ensures that the proposed design of the tank meets safety, performance and cost effectiveness.

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Multidisciplinary Design Optimization of Hydrogen Energy Storage using CFD Simulation

The problem to be addressed: Structured methods for net-zero building design exist in the form of performance simulation based on the first principles. However, the use of these methods requires significant technical expertise and expert design tools. Thus, in practice, key design decisions are made by architects before or without the involvement of simulation consultants. The absence of rigorous building energy performance design results in suboptimum designs and missed opportunities for achieving net-zero emissions.

Originality: The originality of this research is in the development of a conceptual framework of design interventions for net-zero housing design and retrofit that translates each intervention into quantified contributions towards achieving net zero building performance.

Significance: The significance of this approach is in bringing net-zero design and retrofit of houses, that normally requires significant technical expertise, to a wider audience.

Rigour: The research will be based on defining a set of design interventions/design grammar rules and on rigorous analyses of these rules using dynamic performance simulation and multi-objective optimisation, until causal relationships between the grammar rules and their quantification are achieved for different climate conditions. The project will benefit from access to experimental evidence base at the University of Salford’s Energy House Labs.

Potential Contribution to Knowledge: The main contribution to knowledge will be in a replacement of specialist knowledge of building performance simulation with design based on a combination of grammar rules that achieves quantifiable net-zero performance and an expanded deployment of net-zero building design.

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Net-zero building design grammar for wide deployment

Wind-induced instability of square cylinders is one of the most frequently investigated issue in fluid–structure interaction. Vortex-Induced Vibrations (VIV) and galloping are the two main aeroelastic phenomena of slender structures subjected to wind flow that can lead to dynamic instability and large amplitude vibrations of the structures. Most of the research focuses on these phenomena considering acrosswind response. However, this is not the case in practice. Non-across-wind aeroelastic response is a type of structural oscillation that occurs when a structure's structural axes are not perpendicular to the wind. This can happen even if the wind is blowing in a different direction than the structure's cross-section. Only a few research on this topic have been done.

To give further insight into non-acrosswind aeroelastic responses, this project aims to study wind effects on slender structure subjected to wind actions which are not normal to structural axes. 

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Non-across-wind aeroelastic response of slender structures

To reduce the environmental impact of flight in line with the 2050 net-zero goal, a few novel electric propulsion prototypes have been proposed over the past few years, with more aircraft to be developed over the coming years.

Currently, there is a lack of understanding on how to optimally design RPM-regulated propellers that efficiently operate in such multi-propeller architectures, as well as how the complex interactions between these propellers impacts the propulsive efficiency and noise generation of the flight vehicle.

The project work will be conducted along three topics. First, a study will be done into scale effects on multi-point optimised RPM-regulated propellers using blade element methods. Second, various propeller arrangements will be investigated, and empirical models will be derived for the overall propulsive efficiency and noise generation as function of system architecture. Lastly, an in-depth investigation will be done on wake interaction on the most common and promising multi-propeller architectures, either using low-speed wind tunnel testing and/or computational fluid dynamics.

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Propeller and wake interaction in distributed propulsion systems

Machine Learning has been found successful for various real-life applications including medical, finance, autonomous cars, text recognitions, and many others. Similarly, machine learning-based methods have been effective in addressing agricultural problems.

The main goal of this PhD project is to develop machine learning models that can address and segment between crops and weeds. The specific objectives of this project are as follows:

· Detect and segment crops and weeds in a real agricultural environment.

· Optimise the performance of state-of-the-art machine learning models.

· Develop a robust machine learning model that can be validated in different scenarios.

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Segmentation of Crop Weed Discrimination by Machine Learning

The soundscape can be described as a complex auditory environment, comprising a variety of sound sources that originate from a particular landscape. These sounds may affect how people experience the landscape. The way in which humans experience the soundscape may be influenced by social and cultural characteristics including their background and previous experiences.

Individuals will experience a unique ‘daily soundscape’ which is likely influenced by a variety of social and cultural characteristics, including community interactions, socioeconomic status, cultural norms, and personal history.

This PhD will employ primarily innovative qualitative methods (e.g., walking interviews, participant/photo diaries) and some quantitative methods (e.g., hearing tests, noise mapping) to understand how social and cultural characteristics influence participants' ‘daily soundscape’. 

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Social and cultural influences on the experience of soundscapes

There are increasing reports which show wind-induced damages to temporary structures such as fences, scaffolds, etc. Such damages might directly or indirectly lead to human injures, interruptions of transportation, safety, consequently affecting society.

Although temporary structures are present everyday and everywhere, guidelines and code of practice on how to estimate wind effects, e.g. wind loading, on those structures are very limited and vague. For example, only a few types of temporary structures were addressed. A few wind velocity conditions were considered. Drag coefficient data are not rich enough. So, limited data and guidelines have resulted in challenges in estimate wind loading on the structures so that safety can be guaranteed.

To address the challenges, this project aims to study wind effects on temporary structures, focusing on evaluating wind loading on typical structures. The effects of wind directions, turbulence and structural shapes will be considered. 

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Wind effects on temporary structures

The proposed research project seeks to address the critical challenge of integrating renewable energy sources and electric vehicles (EVs) into existing multi-microgrid systems to advance global decarbonization goals. By leveraging the transformative potential of artificial intelligence (AI) and machine learning (ML), this project aims to develop innovative solutions that enhance energy sustainability, grid stability, and operational efficiency.

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Integration of Renewable Energy and Electric Vehicles into Existing Multi-Microgrid Systems: A Decarbonization Impact Analysis Using AI and ML Techniques

Reactive oxygen species (ROS), like superoxide and hydrogen peroxide, are formed during aerobic respiration in bacterial cells and may induce oxidative damage, either directly or after formation of even more reactive species including hydroxyl radicals. Spin trapping is a popular technique for the detection of unstable and short-lived free radicals. In this method, free radicals are trapped to produce a stable compound that can then be detected.

The aim of these projects is to detect spin trapped free radicals produced by bacteria using mass spectrometry methods. As part of the project, students will receive training in the handling of bacterial cell cultures and the use of instrumentation such as gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS).

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Analysis of spin-trapped free radicals in bacterial cells using mass spectrometry techniques

Biofilm-associated infections pose a significant challenge in healthcare and industrial settings due to their inherent resistance to antimicrobial agents. At the NanoLAB in Salford, we are investigating how advanced nanoparticle design influences antimicrobial properties and biofilm inhibition. Tailored functionalisation of nanoparticles and the formation of the biomolecular corona upon interaction with biological environments have been shown to influence their antimicrobial activity. However, the precise relationship between nanoparticle properties, corona composition, and biofilm formation remains poorly understood.

Would you like to join us in pioneering new strategies to combat antimicrobial resistance through nanotechnology? This interdisciplinary PhD project aims to design and characterise plasmonic nanoparticle libraries and functionalise their surfaces to systematically investigate their antimicrobial properties and the role of biomolecular corona formation in biofilm development. The findings from this research will contribute to the rational design of nanoparticle-based antimicrobial strategies, with further applications in medicine, biotechnology, and environmental science.

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Design of Novel Antimicrobials via Functionalised Nanoparticle Libraries: Advanced Materials Design Against Antimicrobial Resistance

Atherosclerosis is a progressive vascular disease initiated by endothelial dysfunction. It is characterised by the infiltration of low-density lipoproteins (LDLs) into the subendothelial spaces, where they accumulate and become modified, predominantly by oxidation. Oxidised-LDL activates endothelial cells to secrete inflammatory mediators and to express high levels of adhesion molecules that attract circulating monocytes to the sub-endothelial space, leading to the formation of atheromatous plaques within the arterial wall. As these plaques grow, the risk of rupture and subsequent cardiovascular events highly depends on plaque composition - which includes the extent of plaque calcification (Kawai et al., 2024). Intimal calcification is the formation of mineralised tissue within atherosclerotic plaques, and it is largely driven by the oxidative and pro-inflammatory environment which exists in these plaques (Mitsis et al., 2024). Currently, no anti-calcific treatments exist.

In this project the student will gain a wide range of laboratory skills, including cell culture methods to study the effects of natural compounds on vascular smooth muscle cell calcification in vitro and ex vivo, biochemical and molecular approaches to quantify cytokine production (ELISA), and changes in gene and protein expression (RT-PCR, western, immunofluorescence).

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Evaluation of plant-derived compounds as novel therapies for vascular calcification

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with high recurrence rates, therapy resistance, and poor prognosis. Radiotherapy is a cornerstone of TNBC treatment, but it can inadvertently alter the tumour microenvironment by inducing senescence in cancer-associated fibroblasts (CAFs). Senescent CAFs secrete a complex mixture of pro-inflammatory molecules, growth factors, and proteins in the form of small extracellular vesicles (sEVs), collectively termed the senescence-associated secretory phenotype (SASP). These sEVs can influence TNBC tumour heterogeneity by promoting cancer stem cell (CSC) expansion, invasion, and resistance to therapy.

This project will investigate how CAF senescence impacts sEV composition and their role in modulating TNBC CSC dynamics. This study aims to elucidate the mechanisms by which CAF-derived sEVs contribute to TNBC progression and therapy outcomes.

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Investigating the Role of Cancer Associated Fibroblast Senescence and sEVs in Triple-Negative Breast Cancer Stem Cell Dynamics

The WHO's Global Leprosy Strategy 2021–2030 identifies zoonotic reservoirs as a critical obstacle to leprosy eradication, particularly emphasizing the need for country-specific strategies in endemic regions. Brazil is a focal point for leprosy, accounting for 94% of Latin American cases, with approximately 28,000 new cases reported annually. Understanding the role of zoonotic transmission, especially from armadillos, is crucial for developing effective eradication strategies. Human-to-human transmission is the primary route for leprosy; however, recent studies have highlighted the significant role of armadillos as zoonotic reservoirs, particularly among hunters and consumers. Despite evidence of Mycobacterium leprae and M. lepromatosis infections in armadillos, zoonotic aspects of leprosy remain underexplored in Brazil, limiting public health measures to address the disease comprehensively.

This PhD project aims to:

1. Investigate the contribution of armadillos to leprosy transmission in Brazil using PCR-based assays and whole genome sequencing (WGS).
2. Explore the prevalence and genetic diversity of M. leprae and M. lepromatosis in armadillos and humans.
3. Apply advanced sequencing technologies to enhance the detection, characterization, and genomic profiling of leprosy-causing bacteria.

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Unveiling the zoonotic dynamics of Leprosy in Brazil: a molecular exploration and surveillance approach

The aim of the PhD is to explore mega-projects and the construction industry in general in greater detail from both a UK and international perspective. The project will consider what constitutes project success and failure, the risk factors that need to be considered, how these can (and are) mitigated and lessons that can be learnt to develop a framework approach to improve positive construction and mega-project outcomes in future. Research outcomes will be the development of a new framework(s) applicable to the international and UK construction industries (including any regional variance), and dissemination of findings via reports and publications.

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Considering views of UK, African, and international construction industries

Parametric Architecture, of the 21st century, is well known for its organic forms, complex geometrical shapes, and inspirations from nature. The use of mathematical equations, algorithmic processes and a variety of designing tools, enable architects to implement their ambitious designs. Architects experimented by exploring the strength, mechanics and potential of natural and manmade materials (timber, stone, concrete, steel, glass, ice, etc) to create extraordinary and distinctive designs, that shaped the urban landscapes and enhanced interior spaces. It is timely to consider how Parametric Architecture as a site of future conservation.

This PhD explores a holistic approach in researching Parametric Architecture to identify its future Conservation. The study will focus on understanding the principles of their design concepts, materials, flexibility, adaptability, sustainability, tectonic strategies and construction details. It will explore the current tendencies in conservation, sustainable retrofit, adaptive reuse, and climate change challenges. It will aim to identify future sustainable conservation, criteria, policies and guidelines for parametric architectures.

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Exploring the Conservation of Parametric Architecture

As the 21st century approached, the Northwest region has witnessed unprecedented transformation. The last 30 years have been a continuous journey of regeneration of the region’s industrial heritage and built environment.

This PhD aims to trace the journey of the three decades of Adaptive Resue, Creativity and Challenges, celebrating the local distinctive Architectural Heritage that has been given a new sense of purpose. This new purpose has accelerated the region’s growth, wealth and prosperity, and in 2015 it attracted the governments’ ‘northern powerhouse’ initiative for further development. The region’s regeneration is reflected on the Architectural language and Innovative interventions that are continuously emerging in the built environment. These interventions have social and environmental impact.

The PhD’s objective is to identify the region’s regeneration timeline and milestones, understanding how the Architectural fabric has been adaptively reused, and how the contemporary interventions have been integrated in the urban landscape. The research will focus in identifying the successes and failures in the regeneration process, and the lessons learned that can be applied to other regions in the future.

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Regeneration in the Built Environment, Challenges and Creativity; Exploring Northwest Region’s Regeneration since 1995

According to the International Civil Aviation Organization, land use around airports can impact both operational safety and efficiency of the airport. Activities around an airport which can affect the safe and efficient operation of aircraft and community wellbeing should be taken into consideration when planning land uses in the vicinity of airports. There is a variety of possible land uses which have different sensitivity to aircraft and airports operations, third-party risk and aircraft noise exposure, and compatibility or incompatibility with them. The focus of the proposed research will be on natural, agricultural and recreational land use around airports.

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Airport land use for natural, agricultural and recreational purposes

This PhD project aims to improve safety in construction sites by developing an interactive wind modelling tool that informs scaffolding design and operation. Scaffolding is a crucial but vulnerable structure, highly exposed to wind forces that pose risks to workers and the surrounding area. While current design standards focus on ensuring structural stability in high winds, they do not address the hazards faced by workers operating in dynamic, gusty environments.

Key objectives:

• Develop and validate a real-time LBM-based wind simulation tool for urban environments and scaffolding structures.
• Investigate and validate methods to model scaffolding within the CFD tool.
• Explore coupling with traditional CFD solvers to enhance accuracy while maintaining efficiency.
• Collaborate with industry partners and policymakers to integrate findings into practical applications.

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Enhancing Construction Safety Through Interactive Wind Modelling for Scaffolding

Climate change intensifies gender inequalities, disproportionately impacting women, girls, and marginalized groups through displacement, resource insecurity, and heightened risks of gender-based violence. Despite global frameworks like the Paris Agreement (UNFCCC, 2015) and the Gender Action Plan (UNFCCC, 2017), implementation remains fragmented. For example, 80% of climate-displaced populations are women, yet less than 0.04% of climate finance targets gender equality (IFAD, 2024; van Daalen et al., 2024). Even in high-income nations like the UK, intersectional vulnerabilities—such as energy poverty in female-led households—are overlooked in urban climate strategies (Debnath et al., 2024). These systemic inequities underscore the urgency of reorienting resilience efforts through a gender-responsive lens.

The scope of this study can be narrowed down to a specific area of interest based on the researcher’s focus, while the proposal outlines broader research questions to ensure a comprehensive understanding of gender-responsive climate adaptation. This flexibility allows the study to maintain a clear theoretical and policy foundation while enabling the researcher to concentrate on a particular geographical region, sector, or policy framework.

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Mainstreaming Gender Equity, Gender-Responsive Policies, and Social Inclusion for Sustainable and Climate Resilient Rural and Urban Adaptation

Since the 1970s, research has taken an interest in identifying women practicing as architects. Often, one of the main objectives was to revise narratives that focus on male architects who are portrayed as heroes and genius-designers. However, revising historiography means more than just “adding women and stir’ (Ahrentzen, 1996) because concepts (gender bias) and designations (genius) need to be reconsidered at the same time. 

We seek a graduate in the humanities to work on a PhD project to provide such an overview to women practicing architecture in the UK. While it is important to gather material on individual biographies, the project seeks a broader overview that takes into consideration socio-economic and political parameters. A main objective is to counteract the oversight towards the contributions of women and the persistent dismissal of their contributions and their importance – as individuals and in collaborations – within design processes. This project seeks to contribute to current discourses on women in architecture by exploring social, economic, and political contexts of the works of women.

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Women in UK Architecture, 1860s to 1940s

In the context of global climate change, there is a growing focus on adaptation and mitigation measures applicable to the built environment. Because of the multi-faceted benefits they can provide, the use of nature-based solutions, including green infrastructure, is an increasingly popular approach. Through both retrofitting and incorporation within new buildings, forms of green infrastructure including green roofs and walls can reduce energy costs and mitigate extreme temperatures through insulation, shading, and evaporative cooling. They can also improve urban air quality by trapping atmospheric pollutants, reduce flooding through interception of rainfall, and increase biodiversity by providing a habitat for pollinators and other insects.

Leveraging the School of Science, Engineering & Environment’s recently acquired Cubert ULTRIS X20 Plus hyperspectral camera, this project will explore the potential of hyperspectral imaging for non-invasive assessment of green infrastructure status, with a specific focus on green roofs and walls. The student will benefit from access to the IGNITION Living Lab, which incorporates a range of green infrastructure within the University’s Peel Park campus, where the project will be based.

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Exploring hyperspectral imaging for non-invasive assessment of green infrastructure status

Cities are associated with increased depression and anxiety rates, compared to their rural counterparts, and the adoption of lifestyles that favour the development of non-communicable diseases. This is due, in part, to increased exposure to outdoor air pollution, overcrowding, crime, stressful work, and social isolation, as well as the increased consumption of salt and high-sugar foods, reduced physical activity, and increased tobacco use. To counteract these trends, we must consider how cities can be redesigned to create healthy and happy environments that benefit our physical and psychological wellbeing.

The PhD project will develop new urban strategies that improve health outcomes for citizens by, for example, providing access to balanced diets through urban farming to improve nutrition, providing sufficient access to green spaces at the micro, meso, and macro scales to promote wellbeing and physical activity, providing access to peaceful spaces to recharge when necessaary, creating urban playgrounds for young children, providing access to allotments and garden spaces for those living in apartments to build communities, and many more.

The project can be explored from a health, sustainable development, human geography, or built environment perspective, or a combination of these.

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The Nourishing City: Creating healthy, happy, and nutritious cities

Due to their importance for climate change monitoring, modelling, and adaptation, the Global Climate Observing System (GCOS) designates several vegetation structural properties as ‘essential climate variables’ (ECVs), including leaf area index (LAI) and above-ground biomass (AGB). As a measure of vegetation density, LAI controls the size of the interface between the biosphere and atmosphere, the interception of light, and photosynthesis. Meanwhile, AGB is a vital parameter for carbon budgeting, describing terrestrial carbon sink. Rapidly acquiring millions of point measurements representing the physical environment in three dimensions, terrestrial laser scanning (TLS) has become an increasingly popular method for estimating LAI and AGB in recent years.

This project will evaluate a range of geometric and intensity-based leaf-wood classification approaches, with the aim of better understanding this sensitivity. The project will involve field and laboratory work within the United Kingdom, making use of the School’s newly acquired state-of-the-art RIEGL VZ-600i TLS, as well as the world’s first dual-wavelength, full-waveform TLS, the Salford Advanced Laser Canopy Analyser (SALCA).

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Understanding the sensitivity of vegetation density and carbon storage estimates from terrestrial laser scanning to leaf-wood classification accuracy

According to the Airport Council International, more extreme weather- and climate-related events are expected as the climate continues to change. The frequency, intensity, spatial extent, duration and timing of events are expected to increase. Many airports may remain vulnerable to these events. Airports need to understand the risks and initiate adaptation measures for both existing and new infrastructure to become more resilient to the changing climate. 

The main purpose of this research is to develop a climate-induced adaptation and resiliency framework for airports based on current and emerging best practices. The research project may focus on a certain region or country (e.g., the UK), on one or several potential climate impacts and stressors, on one or several elements of airport infrastructure.

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Climate Change Adaptation and Resilience of Airport Infrastructure