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25 PhD Degree-Fully Funded at University of Liverpool, Liverpool, England

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University of Liverpool, Liverpool, England invites online Application for number of  Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at University of Liverpool, Liverpool, England.

Eligible candidate may Apply as soon as possible.


(01) PhD Degree – Fully Funded

PhD position summary/title: An assessment of non-Newtonian flow in laminar and turbulent mixing flow conditions

This fully-funded PhD project provides a unique opportunity to pursue research in advanced experimental fluid mechanics. The project will study the behaviour of non-Newtonian fluids and, in particular, their behaviour in laminar, turbulent and transitional mixing flow conditions. Starting with an analysis of a yield stress fluid contained within a rotating endwall geometry, the project will use start-of-the-art simultaneous 2D3C Particle Image Velocimetry and Planar Laser Induced Fluorescence to study turbulent flow patterns to enhance our understanding of the fundamental flow physics of non-Newtonian fluids in nature and various industrial processes, including the mixing of wastewater sludge and the flow of cement slurries. There will also be the opportunity to develop numerical models of the flows identified using the University of Liverpool high performance computing resource, the parallel Linux cluster, Barkla.

Deadline : 13 February 2024

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(02) PhD Degree – Fully Funded

PhD position summary/title: Computing over Compressed Data: New Space-Efficient Data Structures for Structure Data

The goal of this project is to lay the theoretical foundation for computing over compressed representations of graph-structured data, to better accommodate memory constraints. As part of larger team, you will use ideas from information theory, data compression, and succinct data structures, (structural) graph theory or the analysis of algorithms towards (1) quantifying the intrinsic information content of graph-structured data, (2) new compression methods for graph-structured data, and (3) designing and analysing space-efficient data structures for certain classes of graphs.

Deadline : 31 December 2023

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(03) PhD Degree – Fully Funded

PhD position summary/title: Curve counting via categorification

This project investigates problems in enumerative geometry, which is one of the most fundamental and classical subjects in mathematics. For example, one can ask a question like ‘How many lines pass through two given points in the plane?’. Sometimes the set of all curves satisfying certain geometric properties is finite, but sometimes not. A modern approach to the enumerative geometry is to assign numbers, called `virtual invariants’ to the spaces parametrising all curves we want to count, whether or not they are finite sets. There are several different virtual invariants, and some of them are introduced by physicists in string theory.

Deadline : 31 January 2024

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(04) PhD Degree – Fully Funded

PhD position summary/title: Development of advanced validation techniques for computational models

This project is part of a 4 year Dual PhD degree programme between the National Tsing Hua University (NTHU) in Taiwan and the University of Liverpool (UoL) in England. As part of the NTHU-UoL Dual PhD Award students are in the unique position of being able to gain 2 PhD awards at the end of their degree from two internationally recognised world leading Universities. As well as benefiting from a rich cultural experience, students can draw on large scale national facilities of both countries and create a worldwide network of contacts across 2 continents.

Deadline : 31 December 2023

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(05) PhD Degree – Fully Funded

PhD position summary/title: Development of proxies for catalytic reactions with high-throughput experimentation and large datasets analysis

This PhD studentship will combine high throughput experimental methods in the synthesis and characterisation of catalysts with automated methods of large dataset analysis to accelerate the discovery of new heterogeneous catalysts for transformations critical to the net-zero economy, such as methanol synthesis from CO2 and green hydrogen production. Libraries of catalysts will be prepared following typical reaction methods, such as impregnation and precipitation, which will be implemented using advanced robotic platforms. The characterisation of products will be done in parallel mode using predominantly diffraction, spectroscopy and thermal analysis techniques. These large-scale characterisation measurements will allow the application of data science methods to build models for catalyst performance. This protocol will generate large datasets in short period of time that will be analysed in batch mode to extract structural, compositional and other properties of the materials. The obtained results will be modelled against the catalytic performance (the catalysis tests will be done at Johnson Matthey) of a selected set of catalysts in order to develop a predictive model, which will be further evaluated and refined by catalysis tests on new sets of samples. The proposed approach allows the exploration of large compositional space of catalysts for key catalytic reactions and enables the development of methods and tools that could be implemented to generate proxy protocols for other catalytic reactions of direct interest to Johnson Matthey. As well as obtaining knowledge and experience in materials synthesis, characterisation and data analysis the student will develop skills in teamwork and scientific communication as the researchers within the team work closely together. The position will appeal to candidates with a strong interest in the synthesis of new materials and catalysis, and in the application of data science and automation methods to chemistry.

Deadline : 29 March 2024

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(06) PhD Degree – Fully Funded

PhD position summary/title: Discovery of inorganic lithium solid electrolytes for all-solid-state batteries

The project will combine synthetic solid-state chemistry, advanced structural analysis and measurement of physical and electrochemical properties of new lithium solid electrolytes, enabling the successful candidate to develop a diverse experimental skillset in materials chemistry and battery chemistry. The focus will be on the discovery of new materials and structures with enhanced performance, accelerated by working with computational design experts. Owing to the multi-faceted nature of this dynamic project, the student will work closely with computer scientists, inorganic (electro)chemists, physicists, engineers, and material scientists, as part of the EPSRC Programme Grant “Digital navigation of chemical space for function”, to discover new solid electrolytes for all-solid-state Li metal batteries.

Deadline :  31 December 2023

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(07) PhD Degree – Fully Funded

PhD position summary/title: Discovery of materials for enhanced PV performance (Ref NSGPVPS2023)

An opportunity for a 3.5 year PhD position supported by NSG Group towards the computational discovery of new materials to enhance the performance of PV devices and forms part of a larger collaboration with NSG around the discovery of new materials for the glass industry.

This PhD project will explore the application of existing computer science methods and algorithms, as well as developing novel ones, to automate the processing of features and their combinations to predict various properties of materials. This may involve developing models to identify new chemistries or regions of the periodic table where these properties may occur, and/or identifying new ways to improve the properties in existing materials.

Deadline : 17 March 2024

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(08) PhD Degree – Fully Funded

PhD position summary/title: Discovery of new materials for applications on glass using Mathematical Optimisation and Machine Learning methodologies (Ref NSGPVCS2023)

This PhD project will study and apply existing optimisation methods and propose methods and mechanisms for novel ones. In particular, focus will be given to black-box optimisation methods capable of handling difficult complex problems that are difficult or impossible to model directly. Examples include grid, coordinate and pattern searches, metaheuristics, model-based methods, surrogate models, evolutionary optimisation methods such as genetic algorithms or ones utilising natural gradient and information geometry, and Bayesian optimisation. Many of them relay heavily on the use of various machine learning and statistical mechanisms to adapt to the optimisation landscape and automatically collect data where applicable.

Deadline :  17 March 2024

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(09) PhD Degree – Fully Funded

PhD position summary/title: Discovery of new optoelectronic materials for coatings on glass towards net zero technologies

This project aims to develop new materials for optoelectronic applications on glass to contribute to the net zero agenda. New materials are required to maintain the pace of efficiency and performance improvements in thin film PV devices, energy saving glazing, electronic displays, lighting and other emerging markets. Previous work in collaboration with NSG has used computational chemistry and deep (machine) learning to predict new material compositions with high optical transparency and high electrical conductivity.

Deadline : 31 December 2023

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(10) PhD Degree – Fully Funded

PhD position summary/title: Discovery of zero thermal expansion materials using Machine Learning and Advanced Data Analytics (Reference NSGZTEML2023)

Low and zero thermal expansion materials are used in many industries where size stability under high temperatures is critical e.g., aerospace, precision manufacturing, sensors. New classes of material are needed to meet intensifying future demands. There are considerable barriers to discovering the required materials. The underlying physics is complex and difficult to predict from first principles, and the space of possible materials is large and equally complex, especially when a combination of properties is needed, i.e., in this case zero thermal expansion and high machinability and durability. Machine learning methods have been successfully applied to many complex problems, and recent work has demonstrated such methods may also be viable to predict new functional materials with desirable properties. For example, neural networks and deep learning methods have attracted attention for their ability to consider complex combinations of multiple attributes/features in a nonlinear fashion to predict structured outputs. This PhD project will explore such methodologies as well as other machine learning and mathematical/statistical data analytics algorithms to model, predict and analyse various properties related to machinable zero thermal expansion materials.

Deadline : 17 March 2024

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(11) PhD Degree – Fully Funded

PhD position summary/title: Enhancing the Detection of Self-Harm and Suicidality in Electronic Patient Record Systems

1. Develop and manage a “gold standard” data set for the annotation of health and social care data to help machine learning and natural language processing systems learn temporal patterns (signatures) in routinely collected health and social care data.

2. Work with clinical colleagues to develop identifiable data “footprints” (or “signatures”) in individual routinely-collected data sources (primary, acute, and secondary mental health and social care data) that provides opportunity for “triangulation” (or, more formally, data fusion) – using natural language processing (NLP) technology

3. Test candidate “signature” detection methods and ways to translate these into usable clinical decision support for clinicians

4. Using appropriate methodology (e.g. action research, participatory design) to iteratively co-design and evaluate interventions that help clinicians better understand who is at risk of self harm and suicide in a timely fashion that promotes active intervention aligned with current clinical guidelines and practice recommendations

Deadline : 12 November 2023

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(12) PhD Degree – Fully Funded

PhD position summary/title: Exploring how complex glycans shape the human gut microbiota and its interaction with the host

This project seeks to understand the molecular mechanisms by which gut bacteria metabolise complex carbohydrates and how this influences their interaction with the host. You will employ an integrated strategy of protein biochemistry, bacterial genetics, metabolomics, and tissue culture to uncover the fundamentals of these metabolic processes seeking how they can be exploited to maximise human health and treat diseases.

Deadline : 27 February 2024

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(13) PhD Degree – Fully Funded

PhD position summary/title: Finding a balance between operational energy, embedded carbon, embedded plastic

Carbon zero targets are often presented in relation to operational energy. Design solutions to achieve these goals alone can, in many instances, result in increased embodied carbon and embodied plastic content and can reduce opportunities for material re-use. This is particularly notable in the construction industry, where solutions to reducing energy demand often include a significantly increase in the use of complex polymer insulations and air tightness tapes.

Deadline : 23 November 2023

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(14) PhD Degree – Fully Funded

PhD position summary/title: Improving core outcome set uptake in trials

Awareness is growing of problems with the choice of outcomes to measure and report in trials, and the waste that this is causing. These include inconsistencies in what and how outcomes are measured across clinical trials; selective reporting of outcomes in some trials; and differences between the outcomes measured in trials and the outcomes that patients consider important. One solution to these problems that is beginning to take hold is for clinical trialists in a particular topic area to measure and report, as a minimum, a core outcome set (COS). A COS is an agreed standardised collection of outcomes that should be measured and reported in a specific area of health.

Deadline : 15 January 2024

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(15) PhD Degree – Fully Funded

PhD position summary/title: Investigating multimorbidity in clinical trials

Clinicians are less likely to prescribe guideline-recommended treatments to people with multiple long-term conditions (multimorbidity) than to people with a single condition [1, 2]. Lack of empirical estimations of treatment effect from RCTs of novel drug treatments have led to uncertainty in treatment recommendations for people with multimorbidity. Participants with comorbidity (the presence of other conditions in addition to specified condition of the RCT, who, by definition, have multimorbidity) are not uncommon in RCTs, but they are under-represented when estimating the treatment effects and drawing treatment guidelines. As the number of people with multimorbidity is rising globally, there is an increasing recognition that clinical guidelines from RCTs should cover patients with multimorbidity [1].

Deadline : 15 January 2024

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(16) PhD Degree – Fully Funded

PhD position summary/title: Machine learning and data science for materials discovery

New materials hold great promise in addressing global challenges that our society faces today. For instance, green hydrogen as a sustainable transport fuel requires the development of materials for better electrolysers to accelerate the transition to clean transportation. Similarly, the realisation of better catalysts will minimise the impact of manufacturing the chemicals and materials that society needs. However, discovering these transformative materials is a challenging and time-consuming task that requires diverse expertise and is plagued by the need for trial-and-error. We need a better way, which means we need to develop better tools.

Deadline : 31 December 2023

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(17) PhD Degree – Fully Funded

PhD position summary/title: Optimised flocculation processes in water treatment

The coagulation and flocculation processes (i.e. the chemical destabilisation of colloidal particles and subsequent agglomeration via mixing) represent two key processes vital to the successful production of potable water. Coagulation brings about a change in the nature of small particles, rendering them unstable, whilst flocculation encourages particle agglomeration via gentle mixing and the formation of irregularly-shaped, loosely connected flocs. Ineffective coagulation or flocculation results in poorer quality feed water to clarifiers and filters, potentially jeopardising treated water quality and increasing operational costs. Despite the importance of optimised coagulation and flocculation for water quality, operational and chemical usage efficiency purposes, flocculator design for water treatment has traditionally been based on empiricism due to a lack of accurate flocculation models. A review of the literature demonstrates that this is an area of research that has been overlooked and consequently the industry lacks robust optimised flocculator design rules based on fundamental understanding of the interrelation between water chemistry, flocculation mechanisms and fluid dynamics. Whilst much previous work in the field has examined optimisation of flocculation chemistry, there is a lack of work which considers both the chemistry and hydrodynamic environment within which flocculation processes occur.

Deadline : 13 February 2024

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(18) PhD Degree – Fully Funded

PhD position summary/title: Plasmonic enhancement of single-molecule charge-transport and optoelectronic response.

In this project, we want to systematically study how the charge transport (i.e. conductance) and photonic behaviour (i.e. light-emission) of single-molecule junctions are altered by the nanoelectrode shape. The research programme will entail contribution from device modelling (using FDTD methods), nanofabrication (using thermal scanning probe lithography techniques, tSPL) and optoelectronic measurements (using bespoke mechanically-controlled break-junction equipment, MCBJ).

Deadline : 14 February 2024

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(19) PhD Degree – Fully Funded

PhD position summary/title: Preparation and Characterisation of ‘Green’ Photocathodes for the Generation of High-Brightness Electron Beams

This project will focus on development of techniques to manufacture high-performance thin-film photocathode electron sources for particle accelerators, modifying and expanding the deposition equipment and processes as necessary. The use of surfaces modified by techniques such as ion implantation will also be investigates. The goal is to identify the optimum materials and preparation techniques to achieve the highest levels of electron beam brightness, with the lowest intrinsic emittance and the longest operational lifetime.

Deadline : 31 January 2024

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(20) PhD Degree – Fully Funded

PhD position summary/title: Redox mediators for long-life lithium-air batteries

This PhD project will focus on the synthesis of redox mediator (RM) families and will initially target derivatives of TEMPO (2,2,6,6-tetramethylpiperidin-1-yl)oxyl) and aromatic amines. Both these families provide a wide scope for design to optimise the efficacy of the RM functionality within Li-O2 cells in order to lower the voltage gap. Li-O2 cells will be fabricated using benchmarked carbon paper electrodes as the air-cathode and lithium metal. Fundamental electroanalytical chemistry through to advanced operando techniques (for example Raman microscopy and differential electrochemical mass spectroscopy) on full cells will be carried out to characterise the nature of the electrochemical reactions.

Deadline : 31 December 2023

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(21) PhD Degree – Fully Funded

PhD position summary/title: Reliable modelling of non-Newtonian sludge flows using novel computational fluid dynamics

The project is interdisciplinary in two dimensions because it brings together experiments and simulations as well as solid and fluid mechanics.  The integration of concepts and technology across these boundaries brings a level of adventure to the project which is countered by building on well-established research in solid mechanics on quantitative comparisons of measurements and predictions using orthogonal decomposition[i],[ii] leading to validation metrics based on relative error[iii] and assessment of measurement of uncertainty[iv]; and in fluid dynamics using experimental techniques to understand turbulent flow regimes[v],[vi],[vii].  IAEA considers the use of CFD and associated validation data in various nuclear design issues and has identified gaps in verification and validation procedures[viii].  The goal of the project will be to develop techniques that allow volumetric, time-varying, flow data from both measurements and predictions to be represented as feature vectors that can be compared using the validation metrics already established in solid mechanics for dynamic events. 

Deadline : 30 June 2024

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(22) PhD Degree – Fully Funded

PhD position summary/title: Robust Video Tracking in Difficult Environments

The project is to contribute to a major Ministry of Defence (MOD) research programme intended to develop generation after next technologies for applications in defence and security, and this project will be co-funded by Thales.

The project will be concerned with developing automated tracking systems for airborne objects in challenging environments. The project will allow the student to acquire expertise in image processing, sensing with electro-optical (EO) and infrared (IR) cameras, video tracking, and sensor data fusion.

Deadline : 1 December 2023

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(23) PhD Degree – Fully Funded

PhD position summary/title: Thin film materials and processes for p-type flexible electronics

A PhD studentship is available for the development of p-type flexible thin film transistor technology. This student will focus of the development of thin film materials and device fabrication processes. In particular, the project will be based on Atomic Layer Deposition technology being developed at Liverpool.

Deadline : 4 December 2023

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(24) PhD Degree – Fully Funded

PhD position summary/title: Ultra High-Field for Enhanced Solid-State NMR of Materials

This studentship will allow a highly motivated candidate to participate in the development of ultra high-field MAS NMR for materials chemistry offering a unique research profile. The successful applicant will join an international and multidisciplinary research team that will provide complete student training, skills and development, ensuring strong employability. The project is based in the Department of Chemistry at the University of Liverpool, which is an international centre of excellence for the chemistry of advanced materials, with ample opportunities to work collaboratively. The successful applicant will have access to state-of-the-art local NMR facilities operating at up to 18.8 T (800 MHz 1H frequency), be able to perform experiments at world-leading large scale NMR research facilities including at the UK High-Field Solid-State NMR Facility (that operates NMR systems at 20 T (850 MHz 1H frequency), 23.5 T (1 GHz 1H frequency) and soon 28.2 T), and expand their research vision and interest by attending (inter)national conferences.

Deadline : 27 November 2023

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(25) PhD Degree – Fully Funded

PhD position summary/title: Upskilling robotic scientists for long-term laboratory workflows

Physical scientists are progressively using AI-driven robotics and automation to accelerate their experiments and discover materials faster. The COVID-19 pandemic and current climate crisis have created an urgency for these scientists and our societies to transform the way materials are discovered for more resilient, flexible pharmaceutical manufacturing and achieving net zero faster. At the University of Liverpool, the mobile robotic chemist has already outperformed human-level performance in finding clean fuels; however, such platforms are nowhere close to being able to carry out the same vast array of experiments that humans are capable of. Hence, to date, a large amount of laboratory experiments still rely heavily on researchers’ manual, repetitive lab work due to the high level of dexterity and skill required.

Deadline : 31 March 2024

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About The University of Liverpool, Liverpool, England –Official Website

The University of Liverpool (abbreviated UOL; locally known as The Uni of) is a public research university in Liverpool, England. Founded as a college in 1881, it gained its Royal Charter in 1903 with the ability to award degrees, and is also known to be one of the six ‘red brick’ civic universities, the first to be referred to as The Original Red Brick. It comprises three faculties organised into 35 departments and schools. It is a founding member of the Russell Group, the N8 Group for research collaboration and the university management school is triple crown accredited.

Ten Nobel Prize winners are amongst its alumni and past faculty and the university offers more than 230 first degree courses across 103 subjects. Its alumni include the CEOs of GlobalFoundries, ARM Holdings, Tesco, Motorola and The Coca-Cola Company. It was the UK’s first university to establish departments in oceanography, civic design, architecture, and biochemistry (at the Johnston Laboratories). In 2006 the university became the first in the UK to establish an independent university in China, Xi’an Jiaotong-Liverpool University, making it the world’s first Sino-British university. For 2021–22, Liverpool had a turnover of £612.6 million, including £113.6 million from research grants and contracts. It has the seventh-largest endowment of any university in England. Graduates of the university are styled with the post-nominal letters Lpool, to indicate the institution.



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