PhD opportunities

Possible research topics to be undertaken in the Infrastructure & Environmet Division of the School of Engineering are given below. If you are interested in any of these projects, you should email the prospective supervisor for discussing your intentions.

The School of Engineering has a limited number of scholarships to offer to excellent candidates, application shall be discussed with the potential supervisor.

Alternatively, you are welcome to identify a different project topic within any relevant research areas by emailing your project proposal to the Head of Division, Dr. Andrew McBride (, who will direct you towards a prospective supervisor with expertise in that area.

Mechanics of Materials and Structures

PhD in Computational Engineering


Prof. Paul Steinmann (


Computational Engineering delivers sophisticated modelling and simulation tools to predict the behaviour of complex, real-world systems. CE has a pervasive impact on engineering design and discovery-led scientific research. Postgraduate studies in CE will equip you with the skills to solve the engineering challenges of the future.

 The Glasgow Computational Engineering Centre (GCEC)is an EPSRC-supported research centre-based at the University of Glasgow. We provide a coherent focus and point of interaction for fundamental and applied research in CE. As a team of ten academics, we have exciting opportunities for motivated and talented students who want to solve challenging and relevant problems across the spectrum of science and engineering. 

 For more information on the research areas of the GCEC and information on our team, visit

Particle tracking in PEPT using machine learning


Dr. Andrew McBride



Positron emission tomography (PET) is a nuclear imaging technique commonly used in nuclear medicine to produce three-dimensional images of functional processes within the body. PET scanners and their underlying algorithms have been adapted to explore the complex flow exhibited by granular systems. In positron emission particle tracking (PEPT), one particle within the system is tagged with a radionuclide. The radionuclide undergoes β+ decay, during which a position and a neutrino are produced. When the position comes into the neighborhood of an electron in the surrounding medium, an annihilation event occurs resulting in the emission of back-to-back photons. The PET scanner detects this pair of back-to-back photons and a line of response is constructed. After sampling over a small time increment, an algorithm determines the position of the particle from multiple lines of response. The trajectory of the particle in 3D space can then be reconstructed.

PEPT provides valuable insight into a range of industrial processes. Examples include the mixing of pharmaceutical powders and the milling of rock. A key assumption is that the behavior of the whole system can be described by that of an individual particle tracked for a sufficiently long time. The ability to track more than one particle simultaneously is therefore of significant value.

Project Summary

The algorithms used to reconstruct the trajectory of a single particle are relatively mature. Recently work has been done to track multiple tagged particles. This provides a far richer data set but presents many challenges. The objective of this research project is to apply recent advances in machine learning to track multiple particles within a laboratory-scale tumbling mill. The generated algorithm should be robust and efficient. Granular flow simulations, using the discrete element method, will be used to augment the experimental data set.

Modelling large deformations in growing soft biological tissu


Dr Prashant Saxena



Understanding mechanicalbehaviour of biological tissues is becoming increasingly important to understand biological function as well as to design effective treatments for medical conditions. Tissues such as human skin are highly deformable and demonstrate nonlinearity in their mechanical response. Phenomena such as wrinkling are associated with mechanical instabilities caused due to large strains. Furthermore all living tissues constantly remodel themselves by replacing old cells and growth of new cells – leading to changes in their mechanical properties.

This project aims at studying the various forms of mechanical instabilities that can occur in tissues due to growth and remodelling. The scope is fairly open and the exact project aims will be finalised in conjunction with your interests. You will have the opportunity to work closely with all the team members at the Glasgow Computational Engineering Centre (GCEC) as well as colleagues in other schools and universities.

This PhD project is suitable for students with interests in topics such as solid mechanics, structural engineering, computational mechanics, applied mathematics, or biomechanics.

Mechanics of smart magneto-active and electro-active material


Dr Prashant Saxena



Electro- and magneto-active smart materials are types of advanced composites that can undergo large deformations in the presence of external electromagnetic fields. Being lightweight and possessing capability of extreme deformations before any fracture, they are excellent candidates to be used as sensors, actuators, vibration suppressors, and in other structural mechanics applications. Modelling of these composites requires dealing with the mechanical and electromagnetic fields simultaneously thereby resulting in a coupled “multi-physics” problem.


This project aims at studying the behaviour of these smart materials close to instability (buckling) – a point where the electro-mechanical or magneto-mechanical behaviour of material changes drastically resulting in extreme deformations. A major outcome of this project will be the prediction of post-buckling response of smart materials. These insights will provide significant value towards design of various devices made of these composites. You will have the opportunity to work closely with all the team members at the Glasgow Computational Engineering Centre (GCEC) as well as colleagues in other schools and universities.


This PhD project is suitable for students with interests in topics such as solid mechanics, structural engineering, computational mechanics, applied mathematics, or electromagnetics.

A first order conservation law framework for solids, fluids and fluid structure interaction


Dr. Chun Hean Lee (


The computational analysis of fluid structure interaction phenomena is widely used these days for a wealth of industrial and physical applications. In particular, the field of biomechanics has observed a surge over the last decade in the application of these computational techniques for the modelling of biological tissues (i.e. heart valves) interacting with biological fluids (i.e. blood). Some of these problems are highly challenging, requiring the modelling of highly deformable (nearly incompressible) solids immersed within a surrounding incompressible Newtonian viscous fluid. In this case, a fast and robust computational framework becomes essential for a successful simulation.

Building upon very recent discoveries (i.e. first order conservation law for solid dynamics) made by the supervisory team, the objective of this PhD is the further development of a novel 3D computational framework with significantly improved properties with respect to the current state of the art. Initial implementation has been carried out in Matlab platform, with very promising results in some extremely challenging solid dynamics problems. Interestingly, the methodology will borrow concepts from Computational Fluid Dynamics and apply them to Computational Solid Dynamics in a way that will greatly enhance the robustness and accuracy of the simulations, with the final aim to handle fluid-structure interaction.

The recruited PhD candidate will become a member of an active research group working on the development and application of cutting edge computational techniques for large strain solid dynamics, fluid structure interaction and computational multi-physics.

Project summary

Traditional low-order finite element formulations are typically employed in Industry when simulating complex engineering large strain fluid structure interaction problems. However, this approach presents a number of well-known shortcomings, namely: (1) unable to accurately capture the initiation and propagation of strong discontinuities in solids/fluids, (2) a reduced order of convergence for strains and stresses, (3) poor performance in nearly incompressible solids and (4) numerical artefacts in the form of shear/bending locking, volumetric locking and spurious pressure modes. 

The aim of this thesis is to develop a unified computational framework for the numerical analysis of fluid structure interaction problems. In this work, a very competitive vertex centred finite volume algorithm will be employed. The solid-fluid coupling conditions on the interface will be solved via a physically based Riemann solver. In addition, for problems involving extremely massive deformations, it may be necessary to re-adapt the mesh to maintain both the mesh quality and the solution accuracy.

Sister project

The sister project, in collaboration between Swansea University and University of Glasgow, will focus on the development of OpenFOAM finite volume solver for fluid structure interaction. Details of this collaborative project can be found at the following link:


  • To have a strong undergraduate and MSc degree (or equivalent) in Engineering, Mathematics, Physics or a related field 
  • To have an enthusiastic attitude to conduct research, being hard-worker and critic 
  • To have a strong background in nonlinear continuum mechanics
  • To demonstrate experience with numerical methods (Finite Volume/Finite Element) 
  • To have a good knowledge of some programming languages such as Matlab and/or C/C++ 
  • To demonstrate experience with parallel programming

Computational cardiovascular biomechanics

Supervisor: Dr. Ankush Aggarwal (

Summary: Almost 30% of all deaths globally are related to cardiovascular diseases. The overall aim of computational cardiovascular biomechanics is to help improve the diagnosis of these diseases (faster, earlier, more precise), provide better surgical outcomes, and design devices that last longer. To achieve that aim, we study the biomechanical properties of tissues and cells comprising the cardiovascular system using a combination of in-vivo imaging, ex-vivo and in-vitro testing, and in-silico modeling. The projects can be divided into model development (at organ and cellular scales) and method development (based on imaging and using data science approaches). A few examples of specific projects are:

1) Multiscale modeling of the heart muscle
2) Modeling of endothelial cells based on in-vitro experiments
3) Developing methods for biomechanical characterisation of tissues from ultrasound images
4) Designing optimal experiments for cardiovascular tissues under uncertainty

During this project, the student will have opportunities to:

  • Develop skills necessary to work at the interface of engineering and biomedical science
  • Publish papers in high-quality journals
  • Present research results at international conferences
  • Learn about nonlinear finite element analysis, nonlinear mechanics, multiscale modeling, image-based analysis, data science, and other numerical techniques
  • Learn about experimental and clinical validation
  • Collaborate with our international academic and industrial partners
  • Interact within the Glasgow Centre for Computational Engineering with other researchers (GCEC) and across departments with biomedical scientists and clinicians

Eligibility: Candidates must have an undergraduate degree in a relevant field, such as Mechanical Engineering, Biomedical Engineering, Civil Engineering or Mathematics, with a minimum 2.1 or equivalent final grade. A background in mechanics and knowledge of finite element modeling would be necessary. Programming skills will be required for computational modeling.

Application: The deadline for applications is 31 May 2021, and the application process consists of two parts:
1) On-line academic application: Go to and click on the ‘Apply now’ tab. Applicants should attach relevant documents such as CV, transcripts, references and a research proposal.
2) The scholarship application: Complete the application form found at the following webpage: and attach a letter of support from a potential supervisor.  Both the application form and supporting letter should be emailed to

Further information: If you are interested or want more information, please contact Dr. Ankush Aggarwal ( before starting the formal application. Please visit Computational Biomechanics Research Group page for more information on our research.

Water and environment

Phylogeny-aware metrics for microbial community assembly driven by ecological and evolutionary principles


Dr Umer Zeeshan Ijaz (
Professor William T. Sloan (


Microbial community surveys often involve alignment and generation of phylogenetic trees using Operational Taxonomic Units (OTUs) or alternatively, Single Nucleotide Variants (SNVs) as an OTU-free approach using different marker genes(16S/18S rRNA, ITS region etc.). These phylogenetic trees in conjunction with species abundances on a sample space are usually employed in distance metrics (such as Unifrac distances) to ascertain geometric sources of variation (e.g., PERMANOVA test) against extrinsic meta data. Recently, phylogenetic-aware alpha diversity measures have seen their utility in exploring stochastic and deterministic nature of microbial community assembly to delineate environmental pressures (e.g., NTI/NRI metrics). This is usually done by looking at how clustered/dispersed the phylogenetic tree is. Indeed, our recent work [1] has shown a switch from competitive to environmental drivers of microbial communities in longitudinal Chicken cecum profile creating a window of opportunity for human pathogens such as Campylobacter to appear. Other recent methodological developments include phylogenetic beta diversity variants such as β-NTI/ β-NRI [2] and various statistical moments on the phylogenetic trees [3]. In view of these recent developments, the main aims of the PhD project are:
a) to consolidate the existing literature on phylogeny-aware metrics for microbial community analyses (borrowed from the latest in numerical ecology);
b) to further develop information theoretic approaches looking at community assembly from a phylogenetic point of view at different granularity (from species to genera to families to taxa up the hierarchy) and by doing so assessing anomalies in the commonly used reference taxonomies;
c) to incorporate models of molecular evolution in phylogeny aware metrics;
d) to develop approaches for concordance of multiple phylogenetic trees all derived from different marker genes (or primers pairs), but for the same sample space;
e) and to develop mathematical/statistical models on phylogeny that give an account of microbial community resilience to external perturbations by presence/absence of specific clades.
The project team also has a vast experience in developing mathematical and statistical models to explain community assembly in microbial communities, for instance, exploration of neutral community assemblage fitting Neutral Community model for prokaryotes to the distribution of microbial taxa (Professor Sloan) [4], and recent work involving Dr Ijaz on fitting the Unified Neutral Theory of Biodiversity with Hierarchical Dirichlet Process (NMGS package [5]). The prospective student, ideally someone with a computational background: will become part of Environmental’Omics lab within the Water & Environment group (School of Engineering); will be given access to high-performance computing facility maintained at Dr Ijaz’s lab; and will be provided numerous datasets from existing and past microbial community studies to test their methods. Further, programming experience in R is required as the secondary aim of the project is to port the developed methods to microbiomeSeq package ( that both Dr Ijaz and Professor Sloan are contributing to.

[1] U. Z. Ijaz. Comprehensive longitudinal microbiome analysis of chicken cecum reveals a shift from competitive to environmental drivers and a window of opportunity for Campylobacter. Frontiers in Microbiology, 9:2452, 2018. DOI: 10.3389/fmicb.2018.02452
[2] J. C. Stergen et al. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME, 6:1653-1664, 2011
[3] C. Tsirogiannis and B. Sandel. PhyloMeasures: a package for computing phylogenetic biodiversity measures and their statistical moments. Ecography, 39(7):709-714, 2016.
[4] W.T. Sloan et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ Microbiol 8: 732–740, 2006.
[5] K. Harris et al. Linking statistical and ecological theory: Hubbell's unified neutral theory of biodiversity as a hierarchical Dirichlet process. Proceedings of the IEEE, 105(3):516-529, 2017.

Mathematical modelling of Natural microbial communities


Dr. Rebeca Gonzalez-Cabaleiro (


Complex communities of microorganisms have the potential to catalyse industrial processes in a cheap and sustainable way. However, there is a lack of understanding on how microbial populations evolve and how different species collaborate or compete for the available resources in the environment. If we cannot understand these interrelationships, we cannot engineer them.

The Water and Environment Research Group at the University of Glasgow aims to understand in a deeper way, microbial communities that are of interest for the biotechnological industry and for remediation of waters or soils. For doing so, we aim to develop and use mathematical models that will link with the experimental work currently develop in the group. In particular, with Individual-based models (IbMs), where the growth of each of the microbial individuals of the community is described, we can study the relationship of the different microbial species, their position in the community and their survival capacity ( We aim to use IbMs as a platform where we can program and see the growth, decay and evolution of each of the microbial species of a population and how their activity changes the environmental conditions affecting other microorganisms present in the same system.

The PhD candidate will work at the frontiers of biology and chemistry using mathematics and programming as tools. She/he must have a passion for bioprocess engineering but also for mathematical modelling. An interest and knowledge on MATLAB programming will be appreciated. She/he will work in close collaboration with other PhD candidates that are engineering the process at laboratory scale and with industrial partners. Internships to other European Universities will be considered.

Profiling active nitrifiers with single cell resolution


Dr Cindy Smith

Prof. Huabing Yin


Nitrification is a central process in the global nitrogen cycle driven by microorganisms. Once assumed a simple oxidation of ammonia to nitrite and then nitrate by two separate but reliant groups of microorganisms, recent work has revealed a myriad of complexities including complete nitrification within a single organism. A complete understanding of the organisms and environmental drivers of nitrification is essential to inform not only ecosystem functioning in the face of climate change but also to deliver sustainable agriculture and environmental biotechnologies. A significant barrier to current understanding is the inability to directly link nitrification activity to the responsible microorganisms within complex microbial communities. We propose to develop a state-of-the art microfluidics platform to label, sort and subsequently identify nitrifiers from within complex environments based on their activity with single cell resolution. The approach exploits the autotrophic nature of nitrifiers that use CO2as their carbon source. By supplying the microorganisms with a heavy labelled 13C isotope a characteristic Raman shift is generated. This Raman shift can be used to sort active from inactive cells.  The aim of this PhD research project is to develop this state-of-the-art platform combining Stable Isotope Probing (SIP), Resonance Raman (RR) and Raman Activated Cell Sorting (RACS) (SIP-RR-RACS) to sort active and inactive nitrifier. Cell sorting can then be coupled to sequencing approaches to further explore he identify and metabolic capabilities of the active organisms under a range of experimental conditions. 

Mining the salmon louse (Lepeophtheirus salmonis) microbiome for novel control targets



The Atlantic salmon aquaculture industry is a key contributor to the UK economy, contributing over £1 billion to the UK economy annually. Prevention and control of the salmon louse, Lepeophtheirus salmonis, is the most significant disease challenge for this industry, for which novel R&D initiatives are urgently needed. Research relating to other arthropods species has focussed on the potential of manipulating microbial populations within the host as strategies for parasite control. A growing body of work indicates that a close association exists between the microbiome and a number of key biological processes of the host, which in turn contribute to the parasite’s health and fitness. The microbiome of the salmon louse remains unexplored and basic research is needed to describe the ‘core’ microbiota associated with it, to establish the functional role of the microorganisms present and if it has a role in the biology of the parasite’s phenotypic traits and fitness. Interfering with the potential symbiotic function of the salmon louse microbiome may shed new light on its biology and aid in the discovery of novel strategies for control of the salmon louse within aquaculture systems.
The aim of this project is to gain knowledge relating to the salmon louse microbiome by: (1) characterising the microbiome of L. salmonis; (2) examining how biological, ecological and/or environmental parameters influence the microbial diversity within the L. salmonis microbiome, and (3) determine if it is possible to manipulate the microbial diversity of the L. salmonis microbiome. The student will analyse the microbiome of L. salmonis, through different life stages, using Next-Generation Sequencing (NGS) of the 16S rRNA gene and localise ‘core’ microbiota using fluorescent in situ hybridization (FISH), with the aim of examining how biological, ecological and/or environmental parameters influence its microbial diversity. They will also exploit shotgun metagenomic sequencing to provide information on relative abundance of each ‘core’ microbial species, and clues of their potential function. This project is a collaboration between University of Glasgow, Moredun Research Institute and Moredun Scientific Ltd.

Development of a HPTLC-SERS system for detection of contaminant in wastewater


Dr Caroline Gauchotte-Lindsay


Context of the Project Hospital wastewaters have been shown to be responsible for the release of drugs and antibiotics to environmental waters. This has potentially severe impacts on environmental microbial populations and could accelerate the development and spread of antibiotic resistance. The project aims to develop a new sensing technique combining Surface Enhanced Raman Scattering (SERS) and high precision thin layer chromatography (HPTLC). Raman scattering is a widely used chemical analysis technique that provides spectra which act as fingerprints of the molecule under study. Its sensitivity can be extraordinarily enhanced by adding metallic nanostructures in the direct vicinity of the molecules reaching sensitivity up to the single molecule level. However, when used in complex system such as wastewaters, SERS provides spectra with multiple peaks corresponding to the whole range of compound found in the sample. HPTLC is a portable chromatography technique, that allow the separation of different compound in a liquid mixture. However, it does not give a chemical identification. Therefore, the combination of both techniques would fill the drawbacks of each and allow the development of small, sensitive, rapid response analytical tools for the exhaustive characterization of pharmaceuticals in water.
Aim of the project The literature reports of some promising results when SERS is performed on HPTLC place where a solution of metallic nanoparticles has been injected on the fluorescent spots revealed by HPTLC. Instead we propose to elaborate HPTLC plates decorated with nanoparticle fabricated with lithography means. Nanofabrication techniques allow to control the size, the shape and the distribution of the nanoparticles on the surface. In Université Sorbonne Paris Nord (Nathalie Lidgi-Guigui), we have developed several techniques of soft lithography for the fabrication of large assembly of nanostructures on all kind of surfaces including insulating or flexible. At the University of Glasgow, we have developed leading edge method for two-dimensional chromatography of complex environmental samples and specialize in multivariate analysis method to interpret analytical signals. The researcher will be based two third of the time at the University of Glasgow and the rest of the time in Paris.
Candidate profile We are looking for a candidate with strong interest in environmental sciences. They will have a strong background in analytical chemistry, materials or physical chemistry, candidates familiar with HPTLC or Raman spectroscopy would be particularly appreciated. The candidate will have a strong motivation for interdisciplinary project. Interested candidates should contact Dr Caroline Gauchotte-Lindsay ( before December 15th as we will be applying for further competitive funding.


Nathalie Lidgi-Guigui ( is associate professor in the Laboratory of Science and Processes of materials (LSPM) in Université Sorbonne Paris Nord

Improving network inference in complex microbial communities: test case using soil bacteria associated with crops


Dr Umer Zeeshan Ijaz (James Watt School of Engineering)

Professor Barbara Mable (Institute of Biodiversity, Animal Health & Comparative Medicine)

Project Description

Background: The discovery of networks is a fundamental problem arising in numerous fields of science and technology, including, biology, communication systems, sociology, and neuroscience. In terms of microbial communities, syntrophies and antagonistic behavior exists amongst members of microbial consortia, either because of environmental pressure (including both biotic and abiotic factors), or because of competition for resources. Continuing technological advancements in development of massively parallel sequencing of millions of reads, accompanied by parallel development of bioinformatics strategies that give unprecedented accuracy, provides an unprecedent opportunity to use these big datasets for understanding the underlying biological processes that govern microbes. Network modeling in microbial community is at an incipient stage, primarily focusing on “co-occurences”, i.e., which network components (microbes) have associations, but do not indicate the order in which they communicate, i.e., causality. Thus, there are critical gaps in our understanding of interdependencies between microbes within complex communities. One applied area where this could be particularly important is in the assessment of how agricultural practices influence the dynamics of soil communities associated with crops. For example, irrigation could promote overall soil health and growth of beneficial microbes but could also inadvertently increase the frequency of pathogens. Building a network framework using such a soil community dataset would thus also help to advance the theory and have a direct application to aiding in the design of management intervention strategies.

Aims: Using data collected as part of a BBSRC consortium grant focused on management of bacterial pathogens of potato crops, the aim of this PhD project is to fuse some of the key concepts from the latest developments in Inverse Problems with those in Network Science, in order to advance the network inferences possible from complex soil communities. Specifically, the objectives are to:

a) develop robust network inference strategies to obtain both static and dynamic networks for microbial communities;

b) develop network-wide statistical measures on the obtained topologies to identify keystone microbial species and the subcommunities they belong to;

c) develop meta-analyses workflows to associate the above statistics with meta-data (including physico-chemical data); and

d) develop interactive visualisation strategies to improve interpretability of the obtained networks.

Training outcomes: The prospective student, ideally someone with a computational/quantitative biodiversity background will be given access to a high-performance computing facility, in order to consolidate existing network inference strategies and advance the field. The specific training outcomes include:

a) proficiency in developing bioinformatics workflows on a high-performance cluster using workflow management systems such as SnakeMake and Nextflow;

b) proficiency in developing statistical toolboxes in R (microbiomeSeq package, see “Supervisory Team”; and

c) proficiency in developing bespoke methods and visualisation tools in Python, Perl, C++, and D3.js.

The end result will be proficiency gained in writing software related to microbial communities on similar lines as a recently concluded PhD project Additionally, Dr Ijaz regularly organizes data hackathons (previously under EU Cost 1103 Action programme) on advancing metogenomics and numerical ecology by inviting leading experts in these fields. These have benefitted over 30 visitors nationally and internationally ( and will enable the candidate to stay up-to-date with the latest methods. The student will be provided with a dataset from the recently funded BBSRC consortium grant (BB/T010657/1; led by the James Hutton Institute) on which both Dr Ijaz and Professor Mable are Co-Is, entitled, “Building a Decision Support Tool for Potato Blackleg Disease (DeS-BL)”. By working on this dataset, the student will gain an in-depth insight into soil ecology.

Supervisory Team: Professor Mable is an evolutionary geneticist whose main focus is on understanding the role of genetic diversity in adaptation to environmental variation, including interactions between hosts, pathogens and other elements of biotic communities. Dr Ijaz has vast experience in developing network inference strategies, for instance, GlobalView software developed by him at University of Oxford implements Probabilistic Graphical Models (those that are based on Markov Random Fields, and some that are based on Dynamic Bayesian Networks). These were initially implemented to work with time series data, however, Graphical Lasso Method [1] was successfully applied to 16S rRNA microbiome datasets. More recently, microbiomeSeq, an R package ( from Dr Ijaz’s lab implements generation of co-occurrence networks through simple correlations as well as Biweight Midcorrelations to infer network topologies, on which subcommunity detection algorithms are applied. Prof. Mable and Dr. Ijaz previously co-supervised an LKAS student, Elizabeth Mittell, who was trained by Ijaz in bioinformatic approaches to resolving community structure of soil bacteria associated with wild cabbages and worked with Mable on quantifying population genetic diversity of hosts using reduced representation sequencing.


[1] U. Z. Ijaz. Graphical Lasso Method for 16SrRNA datasets

[2] Guimera, R., & Amaral, L. A. N. (2005). Functional cartography of complex metabolic networks. nature, 433(7028), 895.

[3] Li, J., & Convertino, M. (2019). Optimal Microbiome Networks: Macroecology and Criticality. Entropy, 21(5), 506.

[4] K. Harris et al. Linking statistical and ecological theory: Hubbell's unified neutral theory of biodiversity as a hierarchical Dirichlet process. Proceedings of the IEEE, 105(3):516-529, 2017.

Null Models to delineate microbial community assembly


Dr Umer Zeeshan Ijaz (James Watt School of Engineering)

Project Description

Background: Whilst microbial community surveys using both short-sequence amplicons and whole-genome shotgun metagenomics offer unprecedented insights into taxonomic and functional ecology of environmental communities, important ecological questions such as “what” drives the observed changes in these communities remain largely unexplored. Unravelling the ecological processes that assemble local communities in response to environmental change or perturbation has always been of great research interest. Traditionally, microbial community assembly is thought to be influenced by either “stochastic” or “deterministic” processes. Stochastic neutral theory assumes that species respond to chance colonisation, extinction, and ecological drift not because of theirs and their competitors’ traits but rather because of random changes and thus have no interspecific trade-offs (i.e. species are neutral). Niche theory, on the other hand, assumes that site-to-site variations in species composition is determined entirely by their specific traits, local habitat conditions, and interspecific relations, which in turn creates different niches that benefit different groups of species (determinism).

Null models are statistical tests that have been widely used to describe diversity patterns in macroecology and biogeography, acting as pattern-generating models that are based on the randomisation of ecological data or random sampling from a known or imagined distribution. Recent advancements in the broader numerical ecology field have made it possible to apply theoretical macroecological concepts to microbial metagenomics in order to better understand and quantify the mechanisms and patterns controlling the complexity of microbial ecology.

Aims: Using data collected as part of several grants running in Dr Ijaz’s group, the aim of this project is to develop and advance null modeling techniques for microbial community assembly. Specifically, the objectives are to:

  • explore and consolidate null modeling techniques that rely on incidences (presence/absence), abundances, and phylogeny of microbial consortia;
  • optimise the randomisation procedures by exploiting instruction level parallelism (ILP) utilizing a low-level programming language (traditional null modeling techniques are implemented in R and sometimes take hours and weeks to run); and
  • explore their utility to understand global biogeographical patterns on microbial community datasets at both spatial and temporal scales

Training outcomes: The prospective student, ideally someone with a computational/quantitative biodiversity background will be given access to a high-performance computing facility. The specific training outcomes include:

  • proficiency in applying numerical ecology principles;
  • proficiency in scripting metagenomics workflows on a high-performance computing environment (Orion Cluster:; and
  • proficiency in developing bespoke methods and visualisation tools in R, Python, Perl, and C++.

The end result will be proficiency gained in writing software related to microbial communities on similar lines as a recently concluded PhD project Additionally, Dr Ijaz regularly organizes data hackathons (previously under EU Cost 1103 Action programme) on advancing metogenomics and numerical ecology by inviting leading experts in these fields. These have benefitted over 30 visitors nationally and internationally  ( and will enable the candidate to stay up-to-date with the latest methods.

Supervisory Team:  Dr Ijaz has vast experience in developing and advancing statistical tools for microbial community analyses, for instance, RvLab [1] offers an intuitive virtual environment interface enabling users to perform analysis of ecological and microbial communities based on underlying parallel implementation of numerical ecology methods; and microbiomeSeq is an R package ( with easy to use functions to explore diversity patterns and also includes enrichment analyses.


[1] C. Varsos, et al. Optimized R functions for analysis of ecological community data using the R virtual laboratory (RvLab). Biodiversity Data Journal, 4:e8357, 2016. DOI:10.3897/BDJ.4.e8357.

Development of meta-analyses framework for microbial community data


Dr Umer Zeeshan Ijaz (James Watt School of Engineering)

Project Description

Background: Use of high-throughput sequencing is widespread in efforts to understand the microbial communities in natural and engineered systems. These studies (underlying sequences and meta-data) are routinely deposited in online repositories such as the National Center for Biotechnology Information (NCBI) short read archive (SRA) database ( There is a wealth of information held in this available data that goes beyond the more specific questions addressed by any one study and offers promising historical data to complement newer studies. By using and combining these previously published datasets, we can discern large-scale patterns that have the potential to give global perspective. Dedicated repositories such as Earth Microbiome Project (, a massively collaborative effort to characterize microbial life on this planet, is currently underway with their protocols and methods standardized to achieve seamless collation of the newer datasets. Nonetheless, NCBI’s SRA still remains the largest of repositories and  examining its expanse shows that there is huge variation from extraction method right through to bioinformatic workflows. Methodological biases are often introduced at every step – from biomass sampling through to data processing, from the nucleic acid extraction method to the choice of v-region (for short-sequence amplicons). Thus, there are critical gaps in the methodology to collate existing studies without the loss of beta diversity patterns. Additionally, the meta-data associated with the datasets in the recent years have been following an in-depth, controlled description of the samples that microbial community sequences are taken from, and also includes contextual data such as environmental conditions (latitude, longitude, physico-chemical parameters) or clinical observations. Thus, there is an opportunity to use text mining engines to discern patterns based on controlled vocabulary/ontologies.

Aims: Using online repositories for microbial community datasets as a source, the aims of the project are to:

  • develop meta-analyses workflow that seamlessly download existing sequencing datasets from online repositories, and any meta-data associated with existing studies;
  • develop bioinformatics pipelines and methodology to remove inter-study biases in microbial community datasets introduced by variability in terms of sequencing platforms, library protocols, and choice of genomic regions, etc.;
  • develop numerical ecology framework to reveal patterns of interest in a global perspective; and
  • advance the existing text mining strategies (see “Supervisory Team”) by supplementing existing work in Dr Ijaz’s lab with Natural Language Parsers.

Training outcomes: The prospective student, ideally someone with a computational/quantitative biodiversity background will be given access to a high-performance computing facility. The specific training outcomes include:

  • proficiency in applying numerical ecology principles;
  • proficiency in scripting metagenomics workflows on a high-performance computing environment (Orion Cluster:; and
  • proficiency in developing bespoke methods and visualisation tools in R, Python, Perl, and C++.

The end result will be proficiency gained in writing software related to microbial communities on similar lines as a recently concluded PhD project Additionally, Dr Ijaz regularly organizes data hackathons (previously under EU Cost 1103 Action programme) on advancing metogenomics and numerical ecology by inviting leading experts in these fields. These have benefitted over 30 visitors nationally and internationally  ( and will enable the candidate to stay up-to-date with the latest methods.

Supervisory Team:  Dr Ijaz has vast experience in developing meta-analyses workflows and text-mining strategies, for instance, SeqEnv ( [1] and its extension SeqEnv-Ext [2] utilizes Environmental Ontology ( to annotate microbial sequences with knowledge about environments, environmental processes, ecosystems, habitats, and related entities by mining text fields from NCBI repository. More recently, Pytag ( [3] was developed as a general-purpose tool to annotate PubMed abstracts with a far expansive list of ontologies, thus enabling systematic literature surveys.


[1] L. Sinclair, U. Z.  Ijaz et al. Seqenv: linking microbes to environments through text mining. PeerJ, 4:e2690, 2016. DOI:10.7717/peerj.2690

[2] A. Z. Ijaz et al. Extending SEQenv: A taxa-centric approach to environmental annotations of 16S rDNA sequences. PeerJ, 5:e3827, 2017. DOI:10.7717/peerj.3827

[3] O. Koci et al. An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights PeerJ, 6:e5047, 2018. DOI:10.7287/10.7717/peerj.5047

Theoretical ecology of engineered biological systems


Prof Bill Sloan

Project Description

Naturally occurring microbial communities are ubiquitous fundamental catalysts in nature. They inhabit almost every environment on Earth from the deep subsurface to the upper atmosphere and play an essential part in controlling the chemistry of the Earth’s natural environment. Moreover, many years of empirical research has taught us that these barely visible communities can be exploited for the benefit of man. However, our fundamental understanding of how the communities form, evolve and function is poor, which limits our potential to make radical leaps in new emerging technologies for cleaning the environment, power generation, fighting climate change, chemical production and health care.

In this PhD you will explore the fundamental drivers of community assembly in microbial communities that are used in environmental biotechnologies, such as wastewater treatment. Building on established theory and using emerging experimental tools such as microfluidics and flow cytometry you will derive mathematical descriptions of the relationship between the functioning of microbial communities and environmental variables, like the energy in their food  or how open they are to immigration, that can be used in the design of engineering solutions.

The research forms part of a large research initiative on the development of novel biotechnologies for water and wastewater treatment for rural populations in Scotland and in the developing world.

Smart infrastructure

Development of sustainable cementitious binders


Cise Unluer

Funding status



The School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3.5-year PhD project entitled ‘ Development of sustainable cementitious binders’ within the Infrastructure & Environment Division.

With the immensely emphasized importance of addressing sustainability and minimizing the environmental impacts of the construction industry, the sustainability credentials of construction materials are increasingly becoming more important. Currently produced at a rate of >4 Bt/year, Portland cement (PC) is the most widely used construction material in the world, whereas this production rate is expected to double by the middle of the century. The production of cement is responsible for 5-7% of anthropogenic CO2 emissions, presenting a great potential for improvement.

This research project will focus on identifying various solutions to reduce the overall environmental impacts of PC through three main initiatives: (i) partial cement replacements with low carbon materials, industrial by-products and wastes, (ii) improving the overall energy efficiency with the use of alternative raw materials, recycled components and low-energy production methods, and (iii) development of new cement formulations with lower energy consumption and carbon footprints.




Improved ground stiffness measurements for offshore wind turbine foundation design


Dr Róisín Buckley (, Professor Simon Wheeler (


The School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting PhD studentship entitled ‘Improved ground stiffness measurements for offshore wind turbine foundation design’ within the Infrastructure and Environment Division.

The UK is the world leader in offshore wind energy and has set a new ambitious target of 40GW of wind power by 2030, producing enough offshore wind capacity to power every home. Offshore wind turbine (OWT) foundations make up a significant portion of the capital cost of a project and optimisation of their design is paramount. Recent research has led to significant advances through theoretical developments combined with high-quality field-testing. There remains significant uncertainty in the measurement and interpretation of key soil and rock deformation parameters that underpin design approaches. Improving offshore site characterisation will contribute to economising foundation geometries, lowering OWF capital costs and meeting the UK’s renewable energy targets.

A new programme of research in the School of Engineering aims to quantify and reduce the uncertainty associated with soil/rock in situ stiffness measurements and minimise the impact on the predictive capability of OWT foundation design methods. This will be achieved through rigorous measurement and interpretation in the field and the laboratory. This PhD project will use advanced laboratory testing combined with analysis of large industry offshore site investigation datasets to investigate intra- and inter- method variability between laboratory methods to determine both small strain stiffness and stiffness degradation and improve correlation with in situ measurements. The successful student will be involved in the commissioning and use of a new resonant column device within the geotechnical laboratory, funded by Vattenfall Wind Power. There may also be an opportunity for the PhD student to undertake fieldwork within the UK. The research outcomes will be applied to design with an opportunity to reanalyse designed monopile foundations and determine the effect of the results on design life.


The candidate must have a first class, or a strong upper second class, honours degree in civil, mechanical or electrical engineering or a related discipline as well as excellent written and spoken communication skills. Previous experience with scientific programming (e.g. Matlab, Python) and/or soil element testing is also desirable but not essential.


For an informal discussion or for further information on this project, potential applicants are encouraged to contact Dr Róisín Buckley or Professor Simon Wheeler.

Influence of climate on unsaturated soils: laboratory testing and modelling


DrTom Shire (

Professor Simon Wheeler (


The School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3/3.5-year PhD project entitled ‘Influence of climate on unsaturated soils: laboratory testing and modelling’ within the Infrastructure and Environment Division. 

Climate change will lead to more droughts and hotter summers, leading to larger drying and wetting cycles in unsaturated soils. This project will seek to improve our understanding of how these processes will affect the likelihood of geotechnical hazards such as landslides. 

Laboratory testing including an advanced triaxial apparatus and modelling with the leading Glasgow Coupled  Model will be used to improve our understanding of how suction and anisotropy will affect the geotechnical behaviour of unsaturated soils.  The researcher carrying out this project will develop a deep knowledge of unsaturated geotechnics which would be valued by industry as well as high level practical laboratory and problem solving skills.

For an informal discussion or for further information on this project, potential applicants are encouraged to contact Dr Tom Shire or Professor Simon Wheeler:

Enhancing slope stability with capillary barriers


Prof. Simon Wheeler (


Climate change will lead to more extremes of weather, including both droughts and heavy rainfall. This will lead to increased risk of landslides and slope instabilities, as droughts produce cracking of surface soils, providing easier access for infiltrating water during subsequent periods of heavy rain. Infiltrating water dissipates negative pore water pressures (suctions) within the slope and brings the soil from an unsaturated state to a saturated condition, resulting in increased risk of slope failures.
This project will involve advanced numerical modelling of slopes incorporating capillary barriers, to investigate whether these barriers could be effective in reducing rainwater infiltration to the underlying soil, allowing suctions and unsaturated conditions to be maintained and hence enhancing stability. The numerical modelling, employing the CODE_BRIGHT finite element software for multi-physics modelling in unsaturated materials, will include soil-atmosphere interactions, state-of-the art constitutive modelling of water transport in granular soils (including water film flow at low degrees of saturation) and the impact on slope stability. While the project will predominantly involve numerical modelling, key conclusions may be validated by physical model testing in the laboratory.
The project forms a continuation of previous research funded by the EU.