Our PhD students
Current PhD Students
The School of Mathematical Sciences is home to over 90 PhD students. Learn about the experience of some of our current PhD students across various mathematical fields.
I am a second-year PhD student just about to start my third year of study at Queen Mary. My area of research is number theory specifically answering questions about the Fourier coefficients of different families of modular forms, under the supervision of Dr Abhishek Saha. Over the last year, my research has been focussed on Hilbert modular forms whereby I have tried to answer some questions about their Fourier coefficients at cusps. Moving forward, I will be studying Manin's constant and the p-adic valuation of the Fourier coefficients of modular forms at cusps. All this research has been fully supported by the Leverhulme Trust.
Throughout my time at Queen Mary, I have had the chance to be involved in internal seminars and study groups. Since Queen Mary is part of the London Group I have also been able to be a part of the London Analytic Number Theory Study Group and Seminar. This has been a great experience to meet and discuss mathematics with fellow PhD students from other London based universities.
Alongside my research, I have had a number of opportunities to get involved in teaching and outreach both of which have been very useful experiences. These experiences have convinced me that this is something I would like to pursue further in the future.
I am a PhD student at the School of Maths and I am just about to start my second year. My research field is Cancer Evolution and I am investigating extra-chromosomal DNA (ecDNA) evolutionary paths and mechanisms of reproduction, in order to understand connections with cancer development and treatment. I am mainly using numerical and statistical models and my research is deeply based on biological observations and clinical data. My supervisors are Dr Weini Huang and Dr Benjamin Werner and my project is done in cooperation with Barts Cancer Institute of the School on Medicine and Dentistry at Queen Mary University of London.
I started my PhD and moved to London during the pandemic but despite this, I have never felt alone in Queen Mary's academic environment. I was involved in study groups, I could interact and work well with my research team and I attended seminars and conferences which made me feel part of a big community that shares my same interests.
I also had the chance to be involved in teaching opportunities, which allow me not only to improve my communication and academic skills but also to realise how much I am passionate about that. I am deeply grateful to be part of Queen Mary University team, and I could not ask for a better academic experience.
Student Support and Funding
EPSRC Mathematical Sciences Doctoral Training Partnership (DTP) 2021-22 funding.
The EPSRC Mathematical Sciences DTP offers flexible support for PhD students across any areas within the School of Mathematical Sciences. The funding is part of the £300 million in additional funding for mathematical sciences announced by the Government in January 2020. This funding comes as an addition to the main DTP allocation that the School receives every year. The total award for our School is £386,826. This amount is based on our current EPSRC portfolio and it is a reflection of the hard work staff have put in to secure EPSRC funding in the past years. This DTP award will be used to fund four doctoral students starting in 2021-22. The duration of studentships offered through this funding will be 4 years and include the student’s stipend and annual tuition fees, with the flexibility to use some of this funding to appoint international students.
Early-Stage Researcher (ESR) funded by the European Commission Horizon 2020 Marie Skłodowska-Curie European Training Network EvoGamesPlus.
EvoGamesPlus comprises 15 beneficiaries from academic sectors from 9 European countries (Austria, Czech Republic, Germany, Hungary, Ireland, Italy, Poland, Netherlands, UK) and a further 15 partner organisations from the academic sector from 7 countries (Brazil, France, Italy, Spain, Netherlands, UK, USA) and comprises high-level research and doctoral level training programme involving a total of 15 ESRs. Christopher Morison is the ESR appointed at Queen Mary’s School of Mathematical Sciences (SMS). He will join the School in August 2021 and will work under the supervision of Dr Weini Huang (first supervisor), Dr Dudley Stark (second supervisor). The partner training labs will be the Barts Cancer Institute in Queen Mary’s School of Medicine and Dentistry and the Maths Department at the University of Maastricht in the Netherlands. Christopher will conduct a research project to use stochastic processes and game theory to study how different mechanisms of resistance in cancer cells impact patient treatment prospects and how we adjust treatment to maximise treatment efficacy under these different possible assumptions.
Heilbronn Institute for Mathematical Research funded studentship.
The Heilbronn Institute is match funding a four years studentship at Queen Mary. The student appointed to this project will start in October 2021 and will be working on Topological Analysis of Structured Data, supervised by Dr Primoz Skraba. The underlying structure is often critical to understanding and analysing data. The structure can take on many different forms but in real-world scenarios, these structures are often noisy, incomplete and/or approximate. The goal of the project is to understand how topological tools may be used to understand the qualitative structure of systems that exhibit “approximate” structure. For example, many types of data exhibit symmetries or periodicities, and while both are well-understood mathematically, the notion of approximate symmetries or approximate periodicities is not at all well-understood, yet appears in many natural settings e.g. human or animal shapes exhibit approximate left-right symmetry. The project will look at both understanding the mathematical structures which best describe these properties as well as practical issues which arise when dealing with real-world data.