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School of Biological and Behavioural Sciences

A deep learning approach to infer the evolution of Anopheles mosquito populations from genomic data

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. 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 Professor Nichols’ 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 the initiative for Artificial Intelligence in evolutionary genomics, and have the possibility to interact with members of the Digital Environment Research Insitute. Collaborations with other consortia, such as, are expected.

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 also be individually trained by lab members in developing coding, neuroimaging and cognitive neuroscience knowledge and skills.

Project description

Malaria imposes a severe burden on humanity. The most important interventions reducing transmission of the disease use insecticides, targeting the mosquito vectors Anopheles. To get insights into how mosquito populations evolve, expand and move between villages, several programs of genome sequencing for hundreds of samples of Anopheles gambiae and Anopheles coluzzii from various locations in Africa have been completed. However, despite such progress, it is still unknown the extent to which the distribution of insecticide resistance loci is due to independent recurrent mutations or introgression from nearby locations. More specifically, estimating the amount of migration and gene flow between mosquito populations from genomic data is challenging.

We are now able to overcome these issues thanks to the introduction of two powerful computational techniques. Firstly, efficient simulators of realistic population genomic data have allowed for likelihood-free methods to emerge as popular inference tools in population genetics. Secondly, the recent application of Artificial Intelligence, and Deep Learning (DL) algorithms, to population genomic data proved the possibility to infer complex evolutionary scenarios. Among DL algorithms, Generative Adversarial Networks (GANs) are supervised learning methods that have shown great promise to both generate artificial genomes and estimate cryptic population genetic parameters.

The overall aim of this project is to design, implement, train and deploy a GAN for genomic data from multiple Anopheles mosquito populations in Africa to infer past and recent population size changes, as well as estimate migration rates between geographical locations. The student will extend a recently proposed GAN architecture for population genomic data (pg-gan) to incorporate an arbitrary number of populations, including at unsampled locations, with unequal sizes as input. The student will then train the GAN by integrating simulators with extensive genomic data from Anopheles mosquito populations.


This studentship is open to Mexican students applying for CONACyT funding. CONACyT will provide a contribution towards your tuition fees each year and Queen Mary will waive the remaining fee. CONACyT will pay a stipend towards living costs to its scholars.

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. The candidate will have to show good attitude to solving quantitative problems in the area of biological sciences.

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

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 CONACyT 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 CONACyT for the scholarship as per their requirements and deadlines, with the support of the project supervisor.

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

Apply Online

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