Skip to main content
School of Biological and Behavioural Sciences

Integrating palaeogenomics with deep learning to detect selection during recent phenotypic evolution

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 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 PhD student will be member of both Dr Fumagalli and Dr Frantz’s research groups located at Mile End campus. Both labs have strong expertise and track record in bioinformatics, population genetics and data science. The student will be part of and initiatives for Artificial Intelligence and biomolecular archaeological research, respectively, and have the possibility to interact with members of the Digital Environment Research Insitute. During the data analysis phase, the student will be expected to interact with numerous international collaborators including at the Ludwig Maximilian University of Munich, the University of Copenhagen, and the University of Oxford.

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.

The PhD student will receive any additional training in bioinformatics, data science and population genomics from Dr Fumagalli and Dr Frantz. Although this is not the focus of the project, there will also be possiblities to receive additional training in the lab, including in ancient DNA.

Project description

Understanding how species adapt to rapid changes in their environment is critical for biodiversity conservation strategies. The importance of adaptive evolution during rapid phenotypic evolution, however, is still heavily debated. In fact, detecting selection in the genome of an organism that has underwent large demographic changes (i.e. bottleneck or expansion) remains challenging. This make it difficult to predict how species may be able to cope with rapid changes such a climate change.

Recently, we successfully deployed artificial intelligence, and in particular deep learning (DL), algorithms to detect signatures of selection from population genomic data of contemporary samples. Additionally, in recent years, the generation of sequencing data from ancient and historical samples allow for a direction observation of how genetic diversity and allele frequencies change over time.

This project aims to design, implement and deploy a novel DL tool to infer signatures of selection from population genomic data of contemporary and ancient samples. Specifically, the student will design a DL architecture that accommodates for population genomic data from multiple sampling locations and time points as input. The overarching aim is to demonstrate the flexibility of DL algorithms for population genomics whilst shedding novel insights onto the biological mechanisms underpinning rapid genetic adaptation.

We envisage several initial applications to showcase the predictive power and flexibility of the newly developed tool. The student will make use of available published and unpublished data from species that underwent complex demographic history and recent adaptive evolution, including wild and domestic mammals and birds. From these analyses, it will be possible to discern genes under selection during in the past (e.g., during domestication or during ancient climatic events) from genes affected by recent selective processes (e.g., during deforestation, or as a result of artificial selection).


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. 

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.

Eligibility and applying

Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class honours degree in an area relevant to the project. A masters degree is desirable, but not essential.

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

Back to top