Summary
Institut Pasteur is seeking a highly motivated Postdoctoral Researcher for a 2-year, full-time position starting January 2027. This role is part of the ANR-funded UniGEM project, focusing on developing a neural network for estimating epidemiological parameters of P. falciparum using simulation-based inference (SBI) with deep learning. The successful candidate will be responsible for simulating synthetic data to train, develop, and test this neural network.
Postdoctoral Researcher in Synthetic Data Simulation for Malaria Genomic Epidemiology
Designation
Postdoctoral Researcher
Research Area
- Malaria Genomic Epidemiology
- Synthetic Data Simulation
- Simulation-Based Inference (SBI)
- Deep Learning
- Statistical Population Genetics
- Computational Biology
Location
Institut Pasteur, 25-28 rue du Dr. Roux, 75015, Paris, France
Eligibility/Qualification
An ideal candidate will likely have:
- A PhD in statistical population genetics (e.g., prior experience working with ARGs / coalescent-with-recombination), computational biology, bioinformatics, infectious-disease epidemiology, evolutionary or ecological biology, or a closely related field.
- Strong programming skills (R, Python, and/or other).
- Familiarity with version control (Git / GitHub).
- Proficient written and spoken English (B2 level or higher).
- Ability to communicate methodological assumptions and limitations clearly and succinctly.
- Experience with reproducible computational workflows and high-performance computing.
- Experience handling large population-genetic simulations (e.g., SLiM, msprime, tskit).
Job Description
This 2-year, full-time postdoc position is part of the French National Research Agency (ANR) funded project, UniGEM (Unified Inference for Genomic Epidemiology of Malaria), led by Aimee Taylor. UniGEM aims to build a neural network to estimate epidemiological parameters of P. falciparum, the deadliest malaria parasite species, using simulation-based inference (SBI) with deep learning.
Postdoc Work Programme:
- Review literature on epidemiological parameters and existing simulators.
- Benchmark a subset of simulators against key criteria.
- Iterate twice over data simulation: first batch using the most appropriate simulator available, second batch developing an enhanced simulator for refined synthetic data.
- Estimate Bayes optimal error for each batch of synthetic data to guide deep learning goals.
- The first batch of synthetic data will be used by a PhD student (starting October 2027) to initiate deep-learning architecture development.
- The refined batch of synthetic data will be used by the PhD student to finalize the deep-learning model.
Expected Outputs:
- Parameter priors
- Benchmarking results
- A simulation pipeline
- Curated synthetic datasets
- Bayes optimal errors
Publication / Conference:
- Potential for one or two first-author publications (benchmarking study and methodological study on enhanced simulator development).
- Co-authorship on UniGEM publications utilizing the postdoc’s results.
- Encouraged to disseminate findings at the annual American Society of Tropical Medicine and Hygiene conference.
How to Apply
Interested applicants should email Aimee Taylor (aimee.taylor@pasteur.fr), describing their motivation and relevant experience, and providing a CV.
Last Date for Apply
The position will remain open until filled, with applications reviewed on a rolling basis.
Apply Link
Email your application to aimee.taylor@pasteur.fr
