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Two Fully Funded PhD Positions in Climate Dynamics
Understanding Climate Tipping Points Through Earth System Modeling, Machine Learning, and Dynamical
Systems Theory

Who should apply?
We seek highly motivated candidates with a robust quantitative background in computer science, mathematics,
physics, meteorology, oceanography, environmental science, or a related discipline. Experience with scientific
programming (e.g., R, Python, Linux, Fortran, or similar), numerical modeling, statistical methods, or machine
learning is highly desirable. Prior experience with climate or Earth system science is an asset but is not required.
Applicants do not need expertise in all of these areas. The projects are interdisciplinary, and successful candidates
will have opportunities to develop expertise in areas beyond their initial training. The projects are particularly well
suited to candidates who are interested in combining quantitative and computational methods with fundamental
questions about the dynamics of the Earth system.

PhD Project 1: Reduced-Complexity Modeling for Paleoclimate Dynamics
This project will investigate the dynamics of past climate states and abrupt climate transitions. The research will combine Earth system model simulations with the development and analysis of reduced-complexity dynamical models. The student will investigate questions related to climate equilibria, stability, feedbacks, and the mechanisms that can lead to transitions between alternative climate states.

PhD Project 2: Reduced-Complexity Modeling for Future Climate Change




This project will investigate the dynamical response of the Earth system to future anthropogenic climate change. The research will use Earth system models, reduced-complexity dynamical models, and computational and machine-learning methods to explore climate trajectories, stability, feedbacks, and potential tipping behavior under different forcing scenarios.

The two projects will be closely connected, allowing the students to develop complementary perspectives on climate dynamics across past and future climate states.

Research Environment and Training
The successful applicants will enroll in the School of Environmental Studies at Queen's University and join an interdisciplinary research group led by Prof. Christian Seiler. Prof. Seiler is a faculty member in Environmental Studies and is cross-appointed with the School of Computing, providing opportunities to integrate natural and
computational sciences and to develop expertise in scientific computing, machine learning, and Earth system
science. The research will involve computational experiments with an Earth System Model, development and
analysis of reduced-complexity dynamical models, machine learning, numerical methods, and high-performance
scientific computing. Students will have access to high-performance computing resources and opportunities to




collaborate with academic and government research partners in Canada and Europe. The projects are designed
to provide broad interdisciplinary training in climate science, Earth system modeling, dynamical systems theory,
machine learning, and scientific computing, preparing graduates for careers in academic research, government
science, climate modeling, data science, and computational science. Students will be supported in developing a
strong publication record, with an expectation of approximately three peer-reviewed research articles during the
PhD.

Funding and Research Support
The position provides guaranteed minimum funding of $35,000 per year for four years, consisting of a combination
of Queen's Graduate Award, teaching assistantships, and supervisor-supported research funding. Current
domestic tuition and fees are approximately $7,565 per year and are paid by the student from the funding
package. In addition, all reasonable costs associated with conference participation, summer school, and
publication will be covered by the research group. Students who obtain additional competitive scholarships or
awards may receive additional funding in accordance with Queen's University policies.

Eligibility
Due to funding restrictions, these positions are open to Canadian citizens and permanent residents of Canada
only. Queen's University is a research-intensive institution committed to fostering an inclusive and equitable
academic environment. Applications are welcome from all qualified individuals, including women, visible
minorities, Indigenous Peoples, persons with disabilities, and individuals of any sexual orientation or gender
identity.

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📌 Two Fully Funded PhD Positions in Climate Dynamics (Ontario)
🏢 Cgu Ugc
📍 Ontario