The Barcelona Institute for Global Health (ISGlobal) is a cutting-edge institute addressing global public health challenges through research, translation into policy and education. ISGlobal has a broad portfolio in communicable and non-communicable diseases including environmental and climate determinants, and applies a multidisciplinary scientific approach ranging from the molecular to the population level. Research is organized in five programs: Climate, Air Pollution, Nature and Urban Health; Environment and Health over the Lifecourse; Global Viral and Bacterial Infections; Malaria and Neglected Parasitic Diseases and Maternal Child and Reproductive Health. ISGlobal is accredited with the Severo Ochoa distinction, a seal of excellence of the Spanish Science Ministry.

WHAT WE ARE LOOKING FOR
ISGlobal is looking for a Postdoctoral Research Fellow with expertise in mathematical or statistical modelling, or an infectious disease epidemiologist with strong quantitative expertise, to develop and apply computational methods for the project: CONTRA-MAL: Connecting the dots in malaria transmission dynamics: inferring transmission networks by integrating genetics and epidemiology, funded by a Fundación “la Caixa” Junior Leader Fellowship.

The project sits at the interface of pathogen genomic epidemiology, computational and statistical modelling, public health surveillance, and open-source software development. Advanced methods that combine pathogen genomic and epidemiological data have transformed transmission chain reconstruction for viral pathogens. However, these approaches cannot be directly transferred to malaria. Plasmodium parasites have unique biological features, including sexual recombination in the mosquito vector, complex within-host diversity, and, in the case of Plasmodium vivax, dormant hypnozoite stages that can reactivate and cause relapses. These features require new analytical approaches to extract meaningful genetic signals and interpret them in relation to transmission, importation, persistence, and resurgence.

The postdoctoral researcher will work on cutting-edge approaches involving transmission network inference, diffusion-based modelling, statistical and computational epidemiology, and simulation-based validation. The position will involve development of a network-based inferential framework for reconstructing malaria transmission networks from genetic and epidemiological data. The work will include: i) model extensions to accommodate P. vivax epidemiological and genetic data, ii) model testing through simulation and fitting to datasets from studies conducted by international partners, and iii) open-source R package development and reproducible workflows.

The postholder will work closely with Dr Shazia Ruybal-Pesántez, Junior Research Leader in Genomic and Computational Epidemiology and TAGUA Research Group Lead, based within the Malaria and Neglected Parasitic Diseases Program. In addition, the postholder will engage with an active network of collaborators spanning Imperial College London (UK), Menzies School of Health Research (Australia), UNC Chapel Hill (US), and wider regional and global partners, including national malaria programme partners in Africa and the Asia-Pacific region. TAGUA is a recently established interdisciplinary research group working at the intersection of infectious disease surveillance, pathogen genomics, computational epidemiology and applied public health research, with a particular focus on malaria and other vector-borne pathogens.

The position is for 18 months in the first instance, with possibility of renewing depending on funding.

Additional opportunities:

The group actively supports career development of its team members through conference travel, consortia participation, training workshops, mentorship, engagement with senior researchers and international collaborators, and opportunities to develop independent research ideas within the scope of the research programme.

KEY RESPONSIBILITIES
Lead and coordinate research activities related to methodological and R package development
Develop and extend network-based and statistical methods for malaria transmission chain inference from genetic and epidemiological data
Lead the testing, documentation and tutorial development for the open-source R package
Build scalable and reproducible inference pipelines integrating parasite genetic data with epidemiological surveillance data and spatial information
Conduct testing and validation of the methods and frameworks using simulation, and support application to real-world datasets from international partner studies
Publish findings in high-quality peer-reviewed journals and present results at international conferences, consortium or collaborator meetings, and project workshops
Contribute, where appropriate, to grant applications, fellowship proposals, and project-related dissemination activities
Support, where appropriate, supervision and training activities for junior researchers, MSc or PhD students, or visiting collaborators
KEY QUALIFICATIONS
PhD in statistics/biostatistics, computational epidemiology, applied mathematics, infectious disease modelling, computer science, or related quantitative field
Strong quantitative and coding skills, with hands-on experience developing or applying statistical, mathematical, or computational models, and proficiency in R and/or Python
Experience working with complex datasets, such as epidemiological, genetic, genomic, spatial, temporal or other biological datasets
Experience with open-source scientific software development practices, including collaborative coding, documentation, testing, version control, modular pipelines, and reproducible workflows
Track record of peer-reviewed publications, including as lead author or equivalent major research output
Strong communication skills, including fluency in English (written and oral).
Ability and desire to work in collaborative, multi-institutional research settings
Strong interest in public health, infectious diseases and/or vector-borne pathogen epidemiology
DESIRABLE QUALIFICATIONS
Strong interest or experience in pathogen genomic epidemiology, population genetics or bioinformatics
Experience with infectious disease transmission modeling, outbreak analytics, network models, diffusion processes, or spatio-temporal modelling
Experience with probabilistic programming or statistical computing frameworks such as Stan, NumPyro, TensorFlow
Experience developing R packages or other equivalent open-source software, pipelines, or analytical tools
Experience or familiarity with malaria epidemiology, Plasmodium genomics, or P. vivax relapse biology
Experience working with international collaborators, public health partners, and/or surveillance datasets