Hybridization Dynamics and Coancestry Analysis in Spatially Structured Populations: A Computational Approach
Theme: Ecology genetics and evolution
Description: We are embarking on an exciting project aimed at integrating spatially explicit population dynamics with coancestry block analysis. This project presents a unique opportunity to explore the complexities of hybridization in spatially structured populations and its impact on coancestry block lengths. We aim to develop a computational model that simulates hybridization events, tracks coancestry blocks, and analyses their evolution over time. This research has the potential to provide significant insights into population genetics and evolutionary biology.
Additional Information: Roles and Responsibilities:
• Integrate existing population dynamics code with a coancestry block simulation model.
• Implement mechanisms for hybridization and recombination in hybrid offspring.
• Develop efficient algorithms for large-scale simulations on high-performance computing systems.
• Conduct data collection and analysis, focusing on coancestry block changes in hybrid and non-hybrid populations.
• Collaborate closely with the project supervisor and potentially other computational biologists or bioinformaticians.
Qualifications:
• Currently enrolled in or recently graduated from a program in Computational Biology, Bioinformatics, Genetics, or a related field.
• Strong programming skills, particularly in Python and experience with NumPy and other scientific computing libraries.
• An understanding of population genetics, evolutionary biology, and spatial modelling.
• Experience with high-performance computing is highly desirable.
• Ability to work independently and collaboratively in a research environment.
What We Offer:
• An opportunity to work on a cutting-edge research project with real-world applications in genetics and evolutionary biology.
• Access to high-performance computing resources.
• Close mentorship and guidance from experienced researchers in the field.
• Potential for co-authorship in research publications arising from this project.
Application Process: Please submit your CV, a brief statement of research interests, and any relevant work or project samples to d.ortizbarrientos@uq.edu.au. The ideal candidate will start honours in RQ3 2024 or RQ1 2025.
Contact: Professor Daniel Ortiz-Barrientos