Unravelling the Omnigenic Model of Evolution: Integrative Network and Regression Analyses
Theme: Ecology genetics and evolution
Description: Join us in an exciting journey to decode the complexities of the omnigenic model in evolutionary genetics. This project delves into the intricacies of genetic networks, regulatory pathways, and the integral relationship between genes and phenotypic traits. Utilizing advanced computational techniques, we aim to dissect and understand the vast interconnectedness within genetic systems, with a particular focus on evolutionary dynamics.
Additional Information:
Roles and Responsibilities:
• Engage in the development and refinement of computational models exploring the omnigenic theory.
• Conduct integrative analyses combining network theory, regression models, and quantitative genetics.
• Analyse simulated and empirical data sets to infer key evolutionary patterns and genetic interactions.
• Collaborate in interpreting results and preparing them for publication in scientific journals.
Qualifications:
• Enrolment or recent graduation in Genetics, Computational Biology, Bioinformatics, or related fields.
• Proficiency in programming languages such as R or Python, with a keen interest in computational genetics.
• Strong analytical skills, with an aptitude for mathematical modelling and data interpretation.
• Ability to work collaboratively in a multidisciplinary team.
• Curiosity and enthusiasm for evolutionary biology and genetic networks.
What We Offer:
• An opportunity to be part of a cutting-edge research project at the forefront of genetic and evolutionary studies.
• Access to state-of-the-art computational resources and diverse data sets.
• Mentorship from experienced researchers and potential for co-authorship in publications.
• A platform to present findings at conferences and workshops.
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