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  1. Home
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  5. School of Science & Technology Doctoral Studentships
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Students at three-colour laser rig

School of Science & Technology Doctoral Studentships

The School offers a series of doctoral studentships in cutting edge research areas, including topics related with AI, Machine Learning, Renewable Energy, Human Wearable Devices and many others.

You will have the opportunity to be guided by world leading scientists in an exciting international environment in the heart of the City of London.

Moreover, you will be able to fully exploit the power of our new High Performance Computer (Hyperion) and world class laboratories and facilities. Our Doctoral College will support you with further training in diverse transferrable skills and networking opportunities enabling you to complete your doctoral training with an all-round preparation.

Centrally funded studentships

Two studentships in the School are centrally funded by City, University of London, available to UK, EU and international students.

Full-time students will receive a maintenance grant (currently £19,668 per annum) that will rise in line with UKRI stipends, for three years. Tuition fees are also paid.

Project/consumable costs of £1,500 are provided, while each student may also have the opportunity to earn around £2,200 per year on average (maximum of around £4,300 per year) through a teaching assistantship.

Reinventing the future of financial technologies with autistic adults

This PhD studentship is offered in the Department of Computer Science.

Summary

The introduction of digital technologies into personal money management has unintended and sometimes adverse consequences for citizens. These exacerbate financial exclusion and affect disproportionally those who struggle financially or find themselves in vulnerable circumstances. Despite these problems, the discourse surrounding financial technologies continues to be narrowly focused on their purported benefits, with little attention being paid to their shortcomings and, most importantly, to how they could be addressed.

This PhD will research how the downsides of digitising personal finance could be tackled through design. It will do so through participatory design methods and in collaboration with autistic adults.

Participatory design is an approach to technology design that empowers citizens to actively shape and influence the technology-making process. Autistic adults are a group that has been systematically excluded by technology makers and financial service providers. Given the challenging life and financial circumstances of autistic adults, it is important to assess whether existing financial technologies and services are suitable for their needs and preferences, and whether they are resulting in stigmatising or discriminatory treatment.

In addition, collaborating with autistic adults will yield new, diverse, and innovative perspectives that will help researchers critically assess the current state of financial technologies and push the boundaries of their design.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in Computer Science, Design, Sociology, Anthropology or Psychology.

They should demonstrate aptitude for original research and possess a good understanding of digital technologies, design and human-computer interaction. Ideally, the successful candidate should have proven skills in design research and qualitative research methods.

Making an application

Visit our Computer Science research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Belén Barros Pena at belen.barros-pena@city.ac.uk.

Digital Twins for Automated Structural Health Monitoring and Intelligent Maintenance of Floating and Monopile Offshore Wind Turbines

This PhD studentship is offered in the Department of Engineering.

Summary

This project will develop a common digital infrastructure to have a Digital Twin (DT) of both floating and monopile offshore wind turbines. A DT is a cyber-physical system that must represent physical reality at a level of accuracy suited to its purposes. The extent of realism depends on three essentials: modelling, data and visualisation.

In this context, the project will first develop a simulation engine for a realistic numerical representation of the physical behaviour of the assets, combining Finite Elements Analysis (FEA) and multi-physics environments (e.g., wind, waves, earthquakes, etc).

Second, a modern flexible, modular and scalable software architecture will be developed to establish the DT virtual environment, to host and interact with simulation engines.

Third, the DT will be used as a synthetic simulator to produce real-life sensor-like data, of controlled damage and undamaged stages of the assets, for data and Artificial Intelligence (AI) driven solutions for structural health monitoring and damage detection.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in civil engineering or mechanical engineering.

They should demonstrate aptitude for original research and possess a good understanding of structural dynamics, advanced structural analysis, structural health monitoring and FEA. Ideally, the successful candidate should have proven skills in coding (Python and/or Matlab).

Making an application

Visit our Civil Engineering research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Dr Miguel Bravo-Haro at miguel.bravo-haro@city.ac.uk.

The closing date for applications to these centrally funded studentships is 14 April 2023.

When submitting your proposal application, enter the title of the research project and you will automatically be considered for the doctoral studentship. You do not need to submit a proposal as part of your application as the project has already been outlined.

For queries regarding the application process, please contact pgr.sst.enquire@city.ac.uk.

School funded studentships

A total of 15 studentships, funded by the School, are also available across our research areas. Each studentship is for three years and will provide an annual tax-free stipend of £21,000 and 50% of tuition fees.

  • An additional £3,000 is allocated for travel, publications and conference expenses to be used across the duration of the studentship.
  • An extra salary supplement of £1,500 per year will be offered to all successful candidates from underrepresented communities. In particular, the stipend supplement will be reserved for female, LGBTQ+ and disabled applicants.
  • Each student may also have the opportunity to earn around £2,200 per year on average (maximum of around £4,300 per year) through a teaching assistantship.

Studentships are available across all of our Departments.

Department of Computer Science

Six studentships are offered in the Department of Computer Science.

Categorical AI systems

Summary

AI systems, and in particular Deep Neural Networks have obtained impressive results, from defeating professional players at games to the recognition of protein 3D structures. However, they are restricted to specific tasks and domains. In technical terms, they are good at interpolating data, but not at classifying and predicting data dissimilar to their training datasets. That is, AI does not generalize and transfer knowledge.

Such deficit poses, in turn, the question of explainability. Since AI systems don’t show coherent behaviour across comparable domains and tasks, it is difficult to interpret how they operate and thus account for their results.

The project aims at answering the following questions:

  1. Can AI systems that use categorical structures improve generalization and facilitate knowledge transfer across heterogeneous datasets?
  2. Can AI systems with richer representations beyond sets and equivalence relations recognize similarities across different domains and tasks?
  3. Can such systems be a path towards Artificial General Intelligence?
  4. Can we build explainable AI systems based on the formal foundations of category theory?
  5. Can category theory serve as a tool to analyse and interpret the operations of AI systems?
  6. Can category theory be instrumental in building accountable and responsible AI systems?
Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in Mathematics/Computer Science.

They should demonstrate aptitude for original research and possess a good understanding of category theory and machine learning. Ideally, the successful candidate should have proven skills coding in Python.

Making an application

Visit our Computer Science research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Eduardo Alonso at E.Alonso@city.ac.uk.

Using AI Within Online Learning and Assessment

Summary

Sophisticated chatbots are truly disruptive technologies within an educational context – e.g. OpenAI's ChatGPT has caused a great deal of concern within Higher Education, particularly in terms of their potential use to aid students during the assessments.

However, they also offer opportunities in terms of enhancing learning assistance, particularly for unsupervised learning within an asynchronous online course. A student can interrogate a chatbot to help correction of erroneous software code (across many programming languages), including explanation; to obtain sophisticated answers to fairly standard textbook questions; to obtain rudimentary answers to complex problem-based questions. Current chatbot technologies are far more powerful than previously developed technologies, noting a long history of AI-based reasoning engines, going back to domain-specific expert systems in the '80s.

As well as answering questions to providing learning assistance, sophisticated chatbots can also provide questions to students. Within the context of a particular module, targeted personalised questions can be asked to understand a student's learning progression – both in terms of knowledge and skills. For example, if learning programming a student can be challenged to use various language elements correctly, or to construct solutions to problems.

This research project will seek to understand and enhance the capability of chatbot technologies within both the learning and formative assessment within online modules. It will explore how a personalised learning experience can be built within the context of online education. Inter-disciplinary aspects will be considered, including AI, ontology, pedagogy and ethics.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in Computer Science.

They should demonstrate aptitude for original research and possess a good understanding of machine learning algorithms.

Making an application

Visit our Computer Science research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Neil Audsley at neil.audsley@city.ac.uk.

Intelligent Device Authentication (IDA) Mechanism to prevent compromise of IoT devices

Summary

Nowadays, thousands of IoT devices are interconnected and subject to several attacks. For example, these devices can be compromised and act as botnets in large-scale D/DoS attacks or as eavesdroppers that affect citizens' privacy.

This project aims to investigate, design, and develop an authentication mechanism that continuously and intelligently authenticates IoT/smart devices, which can be compromised in time and act as botnets or eavesdroppers.

The authentication mechanism will consider the device (hardware/software), network traffic and behavioural characteristics of IoT devices with the help of learning algorithms and other mathematical approaches. The learning algorithms will assist in building a device profile and observe any anomaly behaviour of IoT devices, signifying potential malicious activity. New mathematical directions (e.g., use Bent functions/Diophantine equations) will be explored for their suitability for creating unique device fingerprints.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in Computer Science or Engineering, with a cyber security focus.

They should demonstrate aptitude for original research and possess a good understanding of network security, linear algebra and machine learning algorithms. Ideally, the successful candidate should have proven skills in coding (python/JAVA), in embedded devices (e.g. raspberry pi).

Making an application

Visit our Computer Science research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Nikos Komninos at nikos.komninos.1@city.ac.uk.

Sour serendipity on social media: Investigating the role of serendipitous information encounters in propagating misinformation on social media

Summary

Misinformation, especially on social media, is one of the greatest online harms of our time and is blamed for many social ills (e.g., vaccine hesitancy, election manipulation, and political unrest). People often do not actively seek misinformation; they passively and serendipitously encounter it (e.g., on their social media feeds). However, the role of serendipity in spreading misinformation on social media has not previously been examined.

This PhD will investigate how and why social media users serendipitously encounter, engage and disengage with this potentially harmful content and its influence on their views and behaviour. It will feed this understanding of the ‘sour’ side of serendipitous information encounters into an explanatory model of the role of serendipity in propagating misinformation on social media and guidelines based on the model to inform government policy, information literacy and/or social media platform design.

This PhD will involve detailed qualitative research (e.g., through a mix of interviews, observations of social media use, and diary studies). There is also scope to (optionally) complement this with quantitative research (e.g., surveys on the prevalence of the problem or experiment-based user studies). This is an opportunity to conduct research of high societal importance and to create a strong research impact beyond academia.

Eligibility and requirements

You should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in Human-Computer Interaction, Information Science or a related discipline (e.g., Computer Science, Psychology, Anthropology, Sociology, Library Science, Information Retrieval).

You should demonstrate aptitude for original research of societal importance. You should also have experience in:

  • Using one or more qualitative data collection approaches, such as interviews, observations, diary studies, and ethnographic approaches
  • Using one or more qualitative data analysis approaches, such as Thematic Analysis, Grounded Theory, Interaction Analysis or a general inductive qualitative approach.
Making an application

Visit our Computer Science research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Stephann Makri at Stephann@city.ac.uk.

Learning from medical images with noisy labels

Summary

Recent deep learning techniques provide unprecedented accuracy in most medical image analysis tasks, but they typically require large image datasets annotated by medical experts. Studies suggest however that expert radiologists can provide suboptimal annotations in up to 30% of the scans (e.g., labelling a healthy image as pathological or incorrectly localising a lesion).

In the presence of these "noisy labels", state-of-the-art techniques:

  1. fail to produce accurate models (due to noisy training samples);
  2. cannot be reliably evaluated (due to noisy test samples).

This project will focus on designing and implementing novel noise-robust learning (NRL) techniques for medical image analysis. These techniques will likely build upon recent self-supervision and multi-task learning methods. The candidate will focus both on classification and segmentation tasks, using both public and clinical medical image datasets provided by project partners (e.g., the University of Chicago). The candidate will also explore how NRL techniques can be used in active learning scenarios to identify a selection of wrongly labelled samples to be relabelled by a clinical expert.

The developed approaches will have a sizeable contribution on the deployment of deep learning models in the clinic in human-in-the-loop settings.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in relevant subjects, including computer science, mathematics, engineering, physics.

They should demonstrate aptitude for original research. The candidate should possess a good understanding of modern machine learning methods (including self-supervised approaches) and computer vision techniques (from convolutional neural networks to vision transformers). Ideally, the successful candidate should have proven skills in coding in Python, and designing and implementing machine learning pipelines.

Making an application

Visit our Computer Science research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Giacomo Tarroni at giacomo.tarroni@city.ac.uk.

Development and Implementation of A Unified Framework for the Lesion Detection and classification of SARS-COV-2 types of viruses in chest CT scans and CXRs

Summary

The centrepiece of the project will be the investigation of early detection of SARS-Cov-2 viruses in clinical conditions, from chest CT scans and x-rays. This project will develop new solutions for segmenting lesions and predicting COVID-19 and other types of pneumonia from chest CT scans using an advanced deep-learning methodology.

Eligibility and requirements

The candidate should have an upper second-class MSc/MEng (or equivalent, or higher) degree in Computer Science, Mathematics, Statistics, Biomedical Engineering or similar.

They should demonstrate aptitude for original research and understand image processing, computer vision, numerical algorithms, computational statistics, machine learning, deep learning, object detection and segmentation well.

Making an application

Visit our Computer Science research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Alex Ter-Sarkisov at alex.ter-sarkisov@city.ac.uk.

Department of Engineering

Six studentships are offered in the Department of Engineering.

Storage Control for Grid Support

Summary

We are now moving to an envisioned future power system which is dominated by environment-friendly distributed resources that are highly intermittent and variable. The proliferation of renewable-based generation requires the use of storage devices to maintain adequate reliability levels. However, energy storage is only useful if combined with intelligent algorithms that determine when and how much to charge or discharge the finite-capacity battery.

As part of this studentship, the use of storage along with renewable generation to operate a highly renewable power system will be investigated. More specifically, the promising Carnot Battery technology will be studied where excess renewable energy is used to increase the temperature of low-grade heat which is stored for later release and conversion to electrical power when required.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in Electrical, Mechanical or Energy Engineering, or Applied Mathematics.

They should demonstrate aptitude for original research and possess a good understanding of optimisation, mathematical modelling, and programming languages (e.g. MATLAB, Python). Ideally, the successful candidate should have proven skills in power systems, frequency control, simulation of thermal systems, techno-economic analysis, and interest in interdisciplinary research.

Making an application

Visit our Electrical and Electronic Engineering research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Dimitra Apostolopoulou (Dimitra.Apostolopoulou@city.ac.uk) or Tala El Samad (T.ElSamad@city.ac.uk).

Optimal compound design of offshore wind turbines with vibration absorbers for weight minimisation

Summary

This project aims to make a step-change to the current design practices of offshore wind turbines (OWTs) used for renewable wind energy generation, by reducing the constructional steel usage in next-gen multi-megawatt turbines (≥15MW) while ensuring their resilience to environmental loads. This is pursued by developing a novel compound optimal design protocol for OWTs fitted with dynamic vibration absorbers (DVAs), in which the OWT tower and the supporting structure are sized for minimum weight together with the optimal tuning of DVAs to meet all design constraints.

The project will tackle the challenges of meeting fatigue and ultimate limit states in large-scale OWTs with ≥200m height under site-specific complex loads by developing novel computationally efficient machine learning and physics-based surrogate models, and by migrating the most recent DVAs from wind-sensitive tall buildings applications.

Eligibility and requirements

The candidate should have at least an upper second-class MEng (or equivalent) degree in civil engineering or mechanical engineering.

They should demonstrate aptitude for original research and possess a good understanding of structural dynamics, fluid dynamics, and engineering optimisation. Ideally, the successful candidate should have proven skills in coding in MATLAB/Python, in structural modelling and dynamic analysis using standard finite element software (e.g. SAP2000, ANSYS, ABAQUS or similar), in applying numerical optimization to structural design problems, and in computational fluid mechanics.

Making an application

Visit our Civil Engineering research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Agathoklis Giaralis (agathoklis@city.ac.uk) or Qingwei Ma (Q.Ma@city.ac.uk).

Acoustic characterisation of perforated plates for the control of thermoacoustic instability

Summary

Thermoacoustic instability is a highly unwanted phenomenon produced by the two-way coupling between flames and acoustic waves. It leads to pernicious oscillations in the combustors of gas turbines, aero engines, and rocket engines which may result in structural damage, reduced energy conversion efficiency, and increased emissions of harmful by-products. The combustion of carbon-free fuels, such as hydrogen, is significantly different to traditional fuels, making them more susceptible to instability. Carbon-free fuels are expected to play a critical role in the decarbonisation of combustion systems and, therefore, suppressing thermoacoustic instability is crucial for a successful transition towards a net-zero economy.

A common technique to reduce the propensity to thermoacoustic instability is the use of perforated plates due to their ability to damp acoustic fluctuations. In this project, we will investigate the acoustic response of perforated plates using a combination of high-fidelity numerical simulations, cost-effective numerical tools, and experiments. This study will inform the design of many engineering systems involving perforations, including the combustors of gas turbines and rockets, leading to the design of more efficient and safer combustion systems.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in mechanical/aeronautical engineering or a related subject.

They should demonstrate aptitude for original research and possess a good understanding of aerodynamics and computation fluid dynamics. Ideally, the successful candidate should have proven skills in numerical simulations, demonstrate excellent project-work and communication skills.

Making an application

Visit our Mechanical Engineering and Aeronautics research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Juan Guzman-Inigo at juan.guzman@city.ac.uk.

Developing data-driven PPG analytical framework for next-generation wearable device: an investigation for blood pressure (BP) estimation

Summary

Let's start with a big question: can we get a person's whole health profile continuously with ease? For example, measure glucose without a prick, obtain blood pressure without a cuff, detect heart arrhythmias, screen for cardiovascular disease biomarkers, and understand skin hydration levels without chunky devices. Photoplethysmogram (PPG) and its ability to quantify and monitor heart rate variability (HRV) have made smartwatches pervasive. However, PPG's full potential for quantifying blood pressure (BP), and other biomarkers relating to our healthcare and well-being and its full capacity for metabolic sequencing are still under exploration.

For example, various studies have used PPG to measure BP, but the results have been inconsistent. The discrepancy in results may be due to the use of different PPG sensors, signal processing methods, and algorithms. There is a great need to develop new methods using PPG that can accurately measure blood pressure and overcome the limitations of the traditional method. We, therefore, propose to conduct this research to establish a ground truth established by clinical experts. Furthermore, to develop a set of advanced signal processing and machine learning analytical pipelines to unlock the potential of using PPG for continuous blood pressure measurement using wearable devices.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in biomedical engineering, computer science, or other related disciplines.

They should demonstrate aptitude for original research and possess a good understanding of physiological measurements (such as PPG), bio signal processing and machine learning algorithms. Ideally, the successful candidate should have proven skills in programming (i.e. python) and a good mathematical foundation.

Making an application

Visit our Electrical and Electronic Engineering research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Caroline Li at caroline.li@city.ac.uk.

Hyperspectral imaging in the monitoring of neonatal generalised oedema and fluid imbalance

Summary

A significant portion of neonates admitted into intensive care receive intravenous (IV) fluid therapy, which often results in fluid overload signalled by the consequent formation of generalised oedema. Complications of IV fluid therapy can lead to mortality and significant morbidity for the patient. At present, assessing if the correct amount of fluid has been given is determined using basic visual assessment, together with documenting weight and fluid intake and output. This technique is limited in various ways and does not prevent cases of generalised oedema. It is also infrequently used as the baby is often too clinically unstable to move around and be weighed daily.

This project proposes the first proof-of-principle of a novel, contact-free method for precise and continuous monitoring of fluid balance using hyperspectral imaging in the Near Infrared region (NIR HCI). The research is in collaboration with a leading Children's hospital (Great Ormond Street Hospital for Children (GOSH) and Ulster University. The project aims to utilise this technique to develop hyperspectral fluid distribution maps in critically ill neonates, as well as build quantitative models for the accurate determination of fluid balance.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in an engineering discipline, ideally Biomedical/Electrical.

They should demonstrate aptitude for original research and possess a good understanding of general optics, instrumentation and multivariate data analysis or machine learning. Ideally, the successful candidate should have proven skills in optical measurements, electronics and coding in python or Matlab.

Making an application

Visit our Electrical and Electronic Engineering research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Meha Qassem at meha.qassem@city.ac.uk.

End-to-end lightweight security protocols for smart home Internet of Things devices

Summary

To bring Internet-grade security to billions of IoT devices, IoT devices must move away from pre-shared keys to digital certificates. New and proposed standards enable IoT devices to implement more lightweight solutions for application layer security, offering real end-to-end security also in the presence of proxies. Using Object Security for Constrained RESTful Environments (OSCORE), IoT devices can communicate securely in a standardized manner using application layer security, which allows messages to traverse proxies to offer end-to-end security.

OSCORE together with Ephemeral Diffie-Hellman over COSE (EDHOC) for key establishment, have the potential to offer lightweight solutions for establishing secure sessions. Hence there is a need for a lightweight and secure certificate enrolment protocol using EDHOC and OSCORE protocols that can traverse proxies without breaking end-to-end security. Combining EDHOC with certificate enrolment is an active area of research and it provides not only authenticated encryption but also provides less message overhead as compared to certificate enrolment over OSCORE.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in Computer Science, Cryptography or Security Engineering.

They should demonstrate aptitude for original research and possess a good understanding of IoT protocols, network security and programming. Ideally, the successful candidate should have proven skills in Phython, C++, Java and Cryptography.

Making an application

Visit our Electrical and Electronic Engineering research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Muttukrishnan Rajarajan at r.muttukrishnan@city.ac.uk.

Department of Mathematics

Three studentships are offered in the Department of Mathematics.

Quantifying human dynamics in decentralised socio-technical systems

Summary

The project aims to quantify and model human dynamics in decentralised socio-technical systems.

Depending on the common interest of the candidate and the supervisor, it will focus either on the dynamics of information production and consumption on social networks (misinformation, polarisation, etc) or on the analysis of cryptocurrency/web3 ecosystems (crypto transaction networks, NFT market, etc).

The project will involve the analysis of large datasets, statistical analysis and, whenever possible, mathematical modelling. In all cases, it will be developed in collaboration with top research institutions, and major partners in the private and/or public sectors.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in mathematics, physics, computer science or similar disciplines.

They should demonstrate aptitude for original research and possess a good understanding of mathematical modelling and data science. Ideally, the successful candidate should have proven coding in python, analysing large amounts of data and, ideally, building and analysing data-driven models.

Making an application

Visit our Mathematics research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Andrea Baronchelli at andrea.baronchelli.1@city.ac.uk or a.baronchelli.work@gmail.com.

Multi-strategy eco-evolutionary models

Summary

Evolutionary game theory describes a process of Darwinian selection of traits or behaviours of individuals. A common assumption in those models is the timescale separation between selection shaping the strategy frequencies and ecology shaping the population size. This is not always realistic, and building on results by Argasinski, Broom and others, we shall consider a framework where games are embedded in the population dynamics.

Eco-evolutionary processes incorporating births and deaths resulting from interactions between different strategists will be studied. Such strategies can include heritable traits or social behaviour with payoffs interpreted in terms of fertility and mortality. We will generalise the work of Argasinski and Broom, the eco-evolutionary stability conditions, to more than two strategies. This will lead to new mathematical and biological insights.

Qualitative analysis of our results in terms of the geometry of phase portraits can reveal important information such as the evolutionary stability of critical points.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in Mathematics or a closely related area.

They should demonstrate aptitude for original research and possess a good understanding of mathematics in general and mathematical modelling in particular; some knowledge of game theory or modelling in biology would be an advantage, but not essential. Ideally, the successful candidate should have experience in computational modelling as well as mathematical techniques.

Making an application

Visit our Mathematics research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Mark Broom at Mark.Broom@city.ac.uk.

Hochschild cohomology of finite group algebras

Summary

The Hochschild cohomology of a finite group algebra is a subtle invariant describing structural properties of blocks of finite groups, closely related to some of the iconic conjectures that drive modular representation theory. This is a graded algebra with some higher structures, one of which is the Lie algebra structure of HH1(A). Empirically, this Lie algebra structure seems to preserve a significant amount of structural information of the original algebra.

The project will aim at solving a number of open conjectures in this area, such as the non-vanishing of HH1(B) of blocks with a nontrivial defect group and the solvability of the Lie algebra HH1(kS_n) of symmetric groups S_n. Furthermore, structural conjectures relate the local structure of blocks to the Lie algebra structure of their Hochschild cohomology.

Eligibility and requirements

The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in Mathematics, ideally an MSc or equivalent from a reputed institution.

They should demonstrate aptitude for original research and possess a good understanding of finite groups and group algebras and their modules; some basic knowledge in homological algebra would be an advantage. Ideally, the successful candidate should have knowledge in some of the areas leading up to this project, such as Lie algebras and/or homotopy theory.

Making an application

Visit our Mathematics research degree web page for further information on making a formal PhD application. You should enter the title of the research project as your proposal when applying.

Initial informal enquiries can be made to Markus Linckelmann at markus.linckelmann.1@city.ac.uk.

Eligibility requirements are listed for each studentship. Exceptionally, if the first degree is in a different subject area, we can consider applications with a good master's degree in a relevant subject or extensive professional experience in the proposed area of research.

Applicants whose mother tongue is not English must meet any one or a combination of the following:

  • A minimum IELTS average score of 6.5, with a minimum of 6.0 in each of the four components
  • The award of a master's degree, the teaching of which was in English from an English-speaking country.

The closing date for applications to these school funded studentships is 31 May 2023 or until places have been filled.

When submitting your proposal application, enter the title of the research project and you will automatically be considered for the doctoral studentship. You do not need to submit a proposal as part of your application as the project has already been outlined.

For queries regarding the application process, please contact pgr.sst.enquire@city.ac.uk.

General guidance

A doctoral candidate is expected to meet the following pre-requisites for their PhD:

  1. Demonstrate a sound knowledge of their research area
  2. Achieve and demonstrate significant depth in at least a few chosen sub-areas relevant to their primary research area
  3. Demonstrate the ability to conduct independent research, including a critical assessment of their own and others' research.

City, University of London is committed to promoting equality, diversity and inclusion in all its activities, processes, and culture for our whole community, including staff, students and visitors. We welcome applications regardless of age, caring responsibilities, disability, gender identity, gender reassignment, marital status, nationality, pregnancy, race and ethnic origin, religion and belief, sex, sexual orientation and socio-economic background. City operates a guaranteed interview scheme for disabled applicants.

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