Description
Overview
Gene drive technologies offer the promise of managing targeted populations for the benefit of public health, agriculture, and the environment. A gene drive system, when introduced into a wild-type population, will rapidly increase toward fixation by subverting normal Mendelian inheritance in favor of the transgene. We are proposing a field test aimed at using gene drive to eliminate human malaria in Africa. The system developed by our team uses an autonomous Cas9-based gene drive to propagate two anti-malaria parasite genes that render the mosquito vector refractory to the parasite and therefore preclude transmission. This is referred to as a population modification or population replacement strategy – eliminating the parasite but not the mosquito.
The Vector Genetics Laboratory (VGL) at the University of California, Davis is a member of the University of California Malaria Initiative (UCMI). The VGL is tasked with moving gene drive technology from the laboratory to the field by conducting trials involving the release of our transgenic mosquito to evaluate its efficacy and safety. We have assembled a team of mosquito ecologists, parasitologists and population geneticists currently at work describing mosquito and parasite biology at our field sites in the islands of São Tomé and Príncipe and the Equatorial Guinean islands of Annobón and Bioko of Equatorial Guinea. Our team includes on-site researchers as well as public health educators and administrators focused on community and regulatory engagement.
Mosquito vectors of malaria are a leading candidate system for the implementation of gene drive technology, which has never been deployed in a field application. Mathematical models predicting gene drive mosquito dynamics when introduced into natural mosquito populations are needed to evaluate strategy feasibility, design field trials, and inform regulatory authorities about efficacy and safety. Data suitable for parameterizing these models have been collected in various VGL experiments, including measures of relative fitness, population size, fecundity, development time, and dispersal ability.
Duties/Responsibilities
Perform independent research focusing on predicting the spread and persistence of transgenic mosquitoes with gene drive in natural populations. The successful candidate will, in collaboration with the PI, develop, formalize, and refine dynamical systems models of gene drive spread and persistence. Responsibilities will include interpreting results, model fitting, model evaluation and validation, and data visualization.
Position will be responsible for the dissemination of research results to both internal and external audiences, including drafting manuscripts for submission to peer-reviewed journals; research presentations at local, national, or international conferences; drafting internal reports for project collaborators; and providing content for outward-facing websites for the lab (VGL), and the consortium (UCMI). Position will serve in a supporting role to presentations by other investigators as directed by the supervisor.
How to apply
Applicants should submit the following materials:
- A cover letter
- A curriculum vitae
- Contact information for 3 referees
Send the above combined into a single PDF with the subject line: “VGL Modeling Postdoc Application” to Christine Coleman (cmhandy@ucdavis.edu)
Review of applications will start immediately. This position will be open until filled. The anticipated start date for this position is early/mid 2026. The appointment is for a duration of two years with a possibility of extension based on funding.
Requirements
Qualifications
We seek candidates with experience in the following research areas: mathematics, statistics, population genetic modeling, demographic modeling, computer science, or closely related fields.
Minimum Requirements:
- Ability to work and communicate effectively in a diverse team environment
- Experience with mathematical/statistical modeling
- Proficiency in a programming language
- A PhD earned no more than five years ago in a related field
Desired Qualifications:
- Experience constructing complex analytical pipelines using Python or R
- Publications in refereed journals and a history of successful research in collaborative team environments
- Experience developing multi-scale models including uncertainty quantification and data fitting
- Knowledge of population genetics or epidemiological modeling
- Familiarity working with large datasets and submitting jobs on Slurm-based computing clusters
