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School of Biological and Behavioural Sciences

Taking identification of ecological networks from population dynamics to the limit

We are seeking applicants for the following PhD opportunity. The successful applicant will join a student cohort in Environment, Biodiversity and Genomics, training together, following an exciting programme designed to inspire the next generation of environmental experts, managers and leaders. They will be equipped to address some of the toughest challenges of our time.

Research environment

The School of Biological and Behavioural Sciences (SBBS) at Queen Mary is one of the UK’s elite research centres, according to the 2014 Research Excellence Framework (REF). We offer a multi-disciplinary research environment and have approximately 150 PhD students working on projects in the biological and psychological sciences. Our students have access to a variety of research facilities supported by experienced staff, as well as a range of student support services.

The successful applicant become part of the research group of Axel Rossberg (Reader in Theoretical Ecology), members of which study a range of topics in theoretical ecology. Work from the group over the last 6 years has been published in high-impact outlets such as Nature Communications, Trends in Ecology & Evolution, Global Change Biology, and Ecology Letters. The student will be co-supervised by Martin Benning (Senior Lecturer in Inverse Problems and Machine Learning).

Training and development

Our PhD students become part of Queen Mary’s Doctoral College which provides training and development opportunities, advice on funding, and financial support for research. Our students also have access to a Researcher Development Programme designed to help recognise and develop key skills and attributes needed to effectively manage research, and to prepare and plan for the next stages of their career.

By your interdisciplinary team of supervisors, you will receive training in advanced community ecology and rigorous statistical and mathematical methods, especially a range of sophisticated regularisation methods to overcome the ill-conditioning of inverese problems.

Project description

Time series of the population sizes of species in ecological communities exhibit what appear at first random patterns of growth and decline. Yet, we know from other lines of evidence (in particular strict limits to the number of species that can locally co-exist), that all these species are coupled through complex networks of strong, deterministic interactions. Better knowledge of these interactions would not only permit more effective management and sustainable use of populations and ecosystems (e.g. tropical fisheries), it would also help us answer fundamental problems of ecology.

There have been many attempts to reconstruct these interaction networks from population time series. However, often these lack scientific rigour. The reliability of the reconstructed networks therefore remains unclear and the question to what extent interaction networks can be reconstructed from ecological time series open.

Complicating this task might be that ecological interaction matrices tend to self-organise to become ill-conditioned. Under the supervision of Axel Rossberg, a theoretical ecologist with a long publication record on interaction networks and Martin Benning, a numerical analyst specialized in the theoretical and computational handling of inverse & ill-posed problems, the student will systematically map out the limits of the what can be learned about interaction networks from population time series. Cutting edge methods for inverse-problem regularisation and use of prior knowlege will applied. Even when individual interactions cannot be identified, higher-level properties of the networks might still be accessible.

The PhD student will consider the problem first for time series generated using existing ecological community models of increasing complexity. Combining different models, the effects of model misspecification will be addressed. This work will allow the student to build a reliable toolbox of methods and gauge the requirements on empirical data for the inference of interactions (with and without informative priors). The student will then demonstrate these methods using publicly available time series data.


This studentship is open to students applying for China Scholarship Council funding. Queen Mary University of London has partnered with the China Scholarship Council (CSC) to offer a joint scholarship programme to enable Chinese students to study for a PhD programme at Queen Mary. Under the scheme, Queen Mary will provide scholarships to cover all tuition fees, whilst the CSC will provide living expenses for 4 years and one return flight ticket to successful applicants. 

Eligibility and applying

Applicants must:

  • Be Chinese students with a strong academic background.
  • Students must hold a PR Chinese passport.
  • Applicants can either be resident in China at the time of application or studying overseas. 
  • Students with prior experience of studying overseas (including in the UK) are eligible to apply. Chinese QMUL graduates/Masters’ students are therefore eligible for the scheme.

Please refer to the CSC website for full details on eligibility and conditions on the scholarship.

Applications are invited from outstanding candidates with a first or upper-second class honours degree and a masters degree in an area relevant to the project (e.g. ecology, biodiversity, environmental science, mathematical modelling).

Applicants are required to provide evidence of their English language ability. Please see our English language requirements page for details.

The deadline for applications to Queen Mary is 30th January 2022. Applicants will need to complete an online application form by this date to be considered, including a CV, personal statement and qualifications. Shortlisted applicants will be invited for a formal interview by the project supervisor. Those who are successful in their application for our PhD programme will be issued with an offer letter which is conditional on securing a CSC scholarship (as well as any academic conditions still required to meet our entry requirements).

Once applicants have obtained their offer letter from Queen Mary they should then apply to CSC for the scholarship by the advertised deadline with the support of the project supervisor. For September 2022 entry, applicants must complete the CSC application on the CSC website between 10th March - 31st March 2022.

Only applicants who are successful in their application to CSC can be issued an unconditional offer and enrol on our PhD programme.

Apply Online


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