24 Funded PhD Programs at University of Strathclyde, Scotland

Are you holding Master’s degree and looking for PhD positions – Fully Funded PhD Programs in UK? University of Strathclyde, Scotland inviting application for funded PhD Programs or fully funded PhD Scholarship. University of Strathclyde is one of the largest university in the world with thousands of employees, students, and research scientists are involved in the innovation of science and technology daily.

University of Strathclyde has huge a campus in Scotland and widely known as for its contribution in top notch education and research. The contribution of University of Strathclyde is not only limited to natural sciences and engineering but it also offers high quality research as well as higher education in bio-medical sciences, social sciences, humanities, psychology, education, architecture etc.

1. Development of chemically safe redox flow batteries

Summary of Doctoral Project:

Redox flow batteries have several advantages for stationary energy storage, which is an essential part of a renewable energy supply system. Conventional flow battery chemistries employ highly toxic and corrosive transition metals such as vanadium, in strongly acidic supporting electrolytes. This adds to the capital cost, especially for recycling at end of life, and makes them unsuitable for deployment in many locations (such as domestic settings) due to environmental risks. This project will develop neutral aqueous organic redox electrolytes suitable for high performance flow batteries with benign chemical properties in terms of safety, cost and end-of-life recycling. Suitable redox active chemicals will be identified and developed to improve solubility, redox kinetics and cycle life; supporting electrolytes and separators will also be investigated, and a demonstration system will be engineered.

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2. Atomic/ molecular insights into the formation of valuable products from waste biomass

Summary of Doctoral Project:

Lignocellulosic biomass is a green alternative fossil resource to produce fuels (e.g. transport fuel, aviation fuel) and value-added products (e.g. light olefins, gases, liquid chemicals). The formation of these products through routes such as pyrolysis and gasification is more advanced rather than traditional burning thus it is promising to address societal demands for ‘green energy’ and sustainability. However, the complicated operating system and inefficient conversion rate largely limit the industrialisation of this process. Within this project, high-efficient solutions are expected to be provided by investigating the mechanism insights of the formation process of these products. Quantum Chemistry and Molecular Dynamics calculations will be carried out to explore the mechanism and kinetics of the process of converting waste into valuable products. This innovated project is ideally suited to students with the creativity and motivation to solve engineering problems using scientific-based principles. The student may expect to build his/her career in the area of bioenergy, sustainable energy and green chemistry.

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3. Foam improved oil recovery

Summary of Doctoral Project:

Typically in oil and gas production, only a small fraction of the oil present manages to flow out of an oil reservoir under the reservoir’s own pressure. Following this, fluids must be injected back into the reservoir to push the remaining oil and gas out. This displacement process is known as “improved oil recovery (IOR)”. Foam is a very promising injection fluid for oil displacement in IOR. It is easy to produce foam in situ within an oil reservoir by injecting alternate slugs of surfactant solution and gas. Moreover foam can have surprisingly low mobility when propagating through a porous medium such as an oil reservoir, meaning it tends to control the motion of all the other reservoir fluids that are present including the oil being displaced.

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4. Modelling the effect of the electric double layer on electron transfer kinetics

Summary of Doctoral Project:

Electron transfer between molecules in solution and a surface (e.g., a metallic electrode or semiconducting substrate) occurs in a wide variety of important areas, including catalysis, corrosion, electrodeposition, photochemistry, etc. The ability to model the electron transfer in these systems is fundamental for the design of practical processes, such as electroplating, fuel cells, catalytic reactors, to name a few. Ions located near a charged surface will lead to the formation of an electric double layer (EDL), which plays significant role in determining the equilibrium and kinetics of electron transfer. In recent years, tremendous advances have been made in the understanding of the EDL, in particular, the influence of charge correlations, which has led to intuitively surprising results, such as like-charge attraction or overcharging, that have been experimentally validated. Despite its importance, current models of electron transfer in solutions still use an overly simplistic description of the EDL.

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5. Intelligent decision system for development of efficient pharmaceutical separation processes

Summary of Doctoral Project:

In the manufacture of high-value chemical products, such as pharmaceuticals, separation and purification from complex, multicomponent mixtures are crucial steps. Crystallisation, often accompanied by other separations steps (extraction, distillation) is nearly always deployed, due to its separation effectiveness and extreme flexibility. These possibilities provide a multitude of choices for selecting the optimal design solution, in terms of product quality and process efficiency, as well as waste production, material and energy consumption. However, the optimal process design requires accurate and reliable thermodynamic data for multicomponent mixtures in the system over a wide range conditions (e.g., solvents, impurity concentrations, temperatures, pressures) which are often missing, especially for new compounds.

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6. Valorisation of wet biomass waste for sustainable production of fuels and chemicals

Summary of Doctoral Project:

Global solid waste production has seen a dramatic increase over recent years. It is recognised that the development of alternative, cleaner sources of fuels and chemicals from wet biomass waste can secure energy sources and address environmental concerns. The technology to convert wet waste into valuable hydrocarbon fuels and chemicals becomes promising, and this relies on unique advancements in fuel process engineering and waste industry design. This project will undertake studies on the process and technology to convert biomass waste to a range of hydrocarbons to optimise the process. The research project requires both modelling and experimental studies to analyse the flexibility of the waste-to-energy process and the fuel properties of hydrocarbons as well as chemicals.

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7. Catalytic reforming of syngas hydrocarbons to maximise hydrogen production

Summary of Doctoral Project:

Hydrogen is an energy source of the future. It is imperative to develop competitive hydrogen production technology from renewable sources such as biomass. Biomass can be converted to hydrogen through gasification to produce syngas. However, tar condenses easily at low temperatures leading to blockages in equipment. This project will develop a novel means to convert tar and other hydrocarbons present in syngas into hydrogen and thus maximise hydrogen production at low cost, which can be subsequently separated from other syngas species. This innovative project is ideally suited to students with the creativity and drive to pursue doctoral studies at a technologically leading university, providing the students to gain expertise in modelling and experimental investigations.

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8. New methods of carbon capture using green materials

Summary of Doctoral Project:

This project focusses on the use of solid, regenerable materials, known as sorbents, to adsorb carbon dioxide and remove it from process streams. The materials produced within the study will be based on sustainable feedstocks, constituting waste products from other sectors, which can be valorised by conversion into adsorbents. Such materials will be subject to pyrolysis to lock in the porous nature of the feedstock, and preventing routes to degradation. There is also scope to physically and chemically modify the materials to enhance their interactions with the target gas. The project builds on work within the Fletcher group [1-3] and will allow the candidate to address the global challenge of reducing greenhouse gas emissions, including carbon dioxide and methane, as well as addressing the production chain for sorbent materials. The candidate will identify and source precursor materials, allowing synthesis of a range of sorbents, their modification and full characterisation.

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9. New methods for removal of pollutants from water

Summary of Doctoral Project:

This PhD project is initially offered on a self-funding basis. It is open to applicants with their own funding, or those applying to funding sources. However, excellent candidates may be considered for a University scholarship. All Strathclyde Postgraduate Research (PGR) students undertake the Strathclyde Researcher Development programme (PGCert), which provides a framework for skills and knowledge development, with the award of the separate qualification in conjunction with the PhD. Additionally, all PGR students are automatically enrolled in the Strathclyde Doctoral School, providing opportunities for students to network and intensifying their research dissemination.

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10. Predicting drug solubility in different solvents using molecular simulation and machine learning

Summary of Doctoral Project:

This project aims to develop a new computational tool to predict the relative solubility of complex multifunctional drug molecules in a wide variety of solvents, including pure liquids, mixtures, supercritical fluids, new “green” solvents like ionic liquids or deep eutectics, and even hypothetical, not yet synthesised solvents. We will achieve this through an innovative combination of molecular modelling, which can predict solvation of small molecules very accurately [2], and advanced machine learning techniques, which can provide sufficient accuracy in a much shorter time frame [3]. By combining the best of physics-based and data-based approaches, the method will strike the right balance between accuracy and computational speed to allow use in an industrial context, while having a strong physical basis to enable rational decision-making.

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11. Digital twin for integration of minewater thermal resources into engineering systems

Summary of Doctoral Project:

The successful candidate will develop a digital twin/digital shadow of the built (surface) and natural (subsurface) environments. The digital twin approach will be designed to optimise the integration of minewater thermal resources for both heat generation and thermal energy storage into the design of domestic and commercial building developments. It will use COWI’s existing Building Information Model (BIM) approach for above-ground infrastructure to incorporate minewater resources into planning from the outset and allow for informed prediction of the carbon footprint of associated construction efforts.

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12. Digital technologies for Resilient & Sustainable Infrastructures

Summary of Doctoral Project:

The PhD student will work closely with our industrial partners to develop innovative digital tools and methodologies for improving the resilience and sustainability of critical infrastructure. These will include but not limited to the development of: mathematical models to address the performance of critical functions and critical hazards; new uncertainty methods with quantifiable confidence; fast simulation tools based on machine learning and engineering-physics based automatic learning.

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13. Assessing the influence of epistemic uncertainties on earthquake insurance metrics

Summary of Doctoral Project:

Two mechanisms are usually employed to manage the implications of earthquakes (or other natural hazards): mitigation and transfer. The first mechanism aims to reduce the risk levels a structure or facility is exposed to and thus mitigate the losses, in addition to increasing the safety levels. This can be achieved, for example, by developing and enforcing seismic provisions in national regulations for the design of new buildings. Improving seismic design standards can sometimes be difficult, since the future benefits are more uncertain compared to the immediate costs involved. In the transfer mechanism, a percentage of the losses is covered by a third party (e.g. an insurance or re-insurance company). Usually, it is agreed that the property owners will still have to cover losses from small events that happen frequently, but they will transfer the responsibility of dealing with the effect of more catastrophic events to the insurer. The proposed PhD project is related to the second of these mechanisms: transfer through earthquake insurance.

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14. AI approaches and automatic uncertainty for robust, reliable, and trustful digital twin: application to sustainable critical infrastructure and systems

Summary of Doctoral Project:

The main aim of the proposed research is to develop robust probabilistic computing tools to support the development of sustainable infrastructure and systems. The research will develop methods for automatic uncertainty characterization and propagation based on probabilistic computing and virtual experts. Probabilistic computing is a promising approach from the AI community for addressing the uncertainties inherent in so-called natural data (or uncertain numbers). The most challenging is the arithmetic using uncertain numbers since the operations themselves introduce dependence, such that after several operations variables are correlated even if beginning as independent (every time a binary operation occurs the computer needs to know how the operand to the left is correlated to the one on the right side of the operator).

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15. PhD in Machine Learning for Reliable Automated Inspection of Aerospace Composites (with an enhanced stipend)

Summary of Doctoral Project:

The project is relevant to the many advanced industrial sectors such as Aerospace, Defence, Automotive & general High-Value Manufacturing striving to bring autonomy to their production/ inspection processes using machine learning. Aligned with the financial commitment from the industrial partner, the project scope is centred around the current NDE demands of Spirit AeroSystems with the target to a) develop and deliver more industry-focused NDE solutions to promote the partner’s and UK’s business growth, and b) to introduce development program for the student, where highly demanded skills by the industry, access to a network of NDE experts in academia and industry, access to the state-of-the-art research facilities, and specialized NDE training can be offered to the student.

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16. PhD: Cognitive intelligence to support explainable, automated industrial decision support

Summary of Doctoral Project:

The aim of this PhD is to demonstrate the unification of both a bottom up, data-driven knowledge graph representation of assessment of condition monitoring data with a top down, domain expert driven representation of the same task, using a formal knowledge modelling methodology, such as commonKADS. The project will also demonstrate the use of the combined knowledge to provide a solution to the engineering task, along with a transparent explanation of the decisions made, the supporting evidence derived from the raw data and associated confidence values for dealing with uncertainty. The project will be contextualized through industry-provided case study data and access to the time of engineers and domain specialists to derive and validated the domain specialist driven component.

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17. Protein adsorption in gel structures simulation, experiment and application

Summary of Doctoral Project:

The aims of the project are to understand protein adsorption in RF gel structures and to tailor gel properties for applications; these include purification steps in protein manufacture, as well as water clean-up. Protein adsorption to materials is an essential step in biotechnology processing, being used to extract proteins from the synthesis broth. It can also be used to clean water by extracting unwanted biological species. At the University of Strathclyde, we have been studying the fundamentals of protein adsorption to model materials, in order to understand how the properties of the material (such as surface charges and hydrophobicity) affect the process. We have also great experience synthesising resorcinol-formaldehyde (RF) gels, and can control porosity and pore sizes through choice of synthesis conditions. We also have experience of applying these materials to a variety of processes including adsorption of unwanted species from water. This project will build on these capabilities to broaden understanding and ultimately create new technology for protein capture.

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18. Designing surfaces to control nucleation in pharmaceutical crystallisation

Summary of Doctoral Project:

This project will use a combination of simulations and experiments to understand and predict how surfaces and interfaces affect nucleation. Experiments will be used to systematically investigate the effect of tunable functionalised surfaces on heterogeneous nucleation of representative organic compounds relevant to the pharmaceutical industry in order to explore the design space of novel heterogeneous nucleants. Characterisation of functionalised surfaces and crystals grown on them will be performed with a suite of advanced characterisation techniques available in the CMAC National Facility (cmac.ac.uk) housed in the Technology and Innovation Centre at University of Strathclyde, including AFM, SEM, Raman microscopy and GI-SAXS. In the simulation part, we will gain a molecular level insight using a combination of quantum mechanical calculations and classical molecular dynamics simulations, which will enable calculation of relative energetics of competing polymorphs on various interfaces corresponding to systems investigated experimentally.

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19. Cleaner Waste Systems through Simultaneous CO2 Capture & Utilisation

Summary of Doctoral Project:

The water and wastewater sector are responsible for around 3% of global greenhouse gas emissions. Every day, large volumes of sludge are generated, which must be treated to reuse standard but the sector is constrained on the amount of waste it can process due to the poor biodegradability of solids. One approach to reducing waste quantities and associated emissions is integrating CO2 removal technologies e.g. coupling electro-methanogenesis, direct interspecies electron transfer materials, exogenous injection of CO2 etc to enhance sludge biological activity.

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20. Solvent reuse to support sustainable pharmaceutical manufacturing

Summary of Doctoral Project:

This project aims to develop and demonstrate solvent recycling methodologies which can be applied within individual process stage during advanced continuous pharmaceutical synthesis and active pharmaceutical ingredient isolation. Typically, pharmaceutical manufacturers use solvents once and then sends them for incineration, this is driven by a desire to minimise risk to product quality. However, this is a major contributor to the industry’s low atom efficiency of around 100kg of hazardous waste per 1kg of product. This research project fits within a wider project on solvent modelling, design and selection operated in partnership between the University of Strathclyde and Imperial College London with support from GSK and Eli Lilly. We have extensive practical experience in continuous pharmaceutical synthesis, isolation and formulation and excellent research facilities, the team works collaboratively with multiple industrial partners (www.cmac.ac.uk) and has a world leading position in this field. This project will build on these capabilities to address this key sustainability challenge for pharmaceutical manufacturing.

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21. Understanding and scale up of secondary nucleation in pharmaceutical crystallisation

Summary of Doctoral Project:

Nucleation of crystals from solution is a crucial part of many manufacturing processes, in global sectors including pharmaceuticals, medicine, foods, chemicals and advanced materials. Nevertheless, despite many years of detailed study, nucleation remains poorly understood at the fundamental as well as the practical application level. This project, co-funded by industrial partners GSK, aims at furthering our understanding of the key role so-called secondary nucleation plays in pharmaceutical manufacturing process design. Secondary nucleation is where new nuclei are triggered and controlled by the addition of pre-existing nuclei to a manufacturing process. This project aims at furthering our understanding of how secondary nucleation works and can be optimised at scales relevant to industrial manufacture.

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22. Active sticky particles

Summary of Doctoral Project:

Active particles are a class of material that has been studied in detail in the past decade, in a wide range of contexts from the transport and collective behaviour of bacteria and algae to nanoengineering with artificial driven nanoparticles and colloids. What is less well known is how the behaviour of systems of active particles is affected by interactions between the particles. Bacteria and algae are known to exhibit complex collective behaviour, but even the basic rules of how simple (attractive, repulsive) interactions change the phase behaviour, aggregation and gelation of simple colloids and particulates is not well understood.

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23. Development of a spatially resolved spectroscopy probe for application in pharmaceutical drying processes

Summary of Doctoral Project:

The project will collaborate with Dr Price’s group to leverage the information gathered and utilise the identified conditions to perform tests on innovative SORS/SR-DRS probe. The student will perform SORS and SR-DRS measurement inline from a drying vessel under a range of drying conditions for combinations of materials and solvents. The collected information will be analysed to develop a robust analysis on estimating particle size distribution and solvent content. Off-line measurements on solvent content and composition will be performed using NMR and GC-MS. The analysis method used for the current SORS and SAR-DRS systems will form the benchmark method and inform the development of an analysis strategy for the proposed SORS/SR-DRS probe. The project is an excellent opportunity for candidates who like to work in a team to develop novel solution with direct influence to address significant industrial challenges.

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24. Multimodal remote sensing and Machine Learning for Precision Agriculture

Summary of Doctoral Project:

The aim of the project is to investigate the use of Machine Learning and Deep Learning methods for the analysis and fusion of data streams to derive consistent and reliable information for farm management. The main challenge will be to integrate the different spatio-temporal resolutions of the data into accurate solutions for farmers. Through continuous engagement with key industrial partners, the project will ensure the relevance of the research and will catalyse the ongoing revolution in the agricultural sector.

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