Postdoctoral Researcher in Exoplanet Modeling and Inference

The University of Toronto invites applications for a postdoctoral researcher to join a research program focused on physics-based modeling and data-driven inference of exoplanets, with an emphasis on planetary interiors, atmospheres, and evolution. The postdoctoral researcher will be part of an international effort to develop next-generation computational frameworks that connect physical models with limited observations. Research topics may span a range of topics, including mitigation of stellar activity in M dwarf systems for transiting atmosphere studies, as well as planetary interiors and evolution across diverse regimes, focusing on small rocky planets and sub-Neptune-sized exoplanets.

Description of duties

• Build and run physics-based models of exoplanet interiors and atmospheres, including linking structure, composition, and thermal evolution.
• Design and execute large model grids and ensembles to explore how observable properties depend on underlying planetary parameters.
• Develop and apply inference frameworks that connect models to data, extracting constraints from sparse, noisy, and heterogeneous observations.
• Implement and maintain modular, well-documented code that can be extended by collaborators and reused across projects.
• Work closely with observers to interpret current datasets (e.g., JWST) and to define model predictions that inform future observations.
• Compare model outputs directly to data, identify mismatches, and iterate on models to improve physical realism and predictive power.
• Contribute to team discussions, papers, and collaborative projects that bridge modeling and observation.

Required Qualifications

• PhD in astrophysics, planetary science, or a related field.
• Strong background in computational modeling and/or statistical inference, with demonstrated experience in scientific programming.
• Experience with planetary interiors, atmospheric modeling, or related physical systems is highly desirable.
• Familiarity with machine learning or emulator-based approaches for accelerating model evaluation is considered an asset.
• Experience working with and managing large observational or simulated datasets, including data pipelines or analysis workflows.
• Ability to work effectively in a collaborative, interdisciplinary environment.