Two Fully Funded PhD Positions in Climate Dynamics

Cgu Ugc; Kingston; On-site; CAD 32,000 - 39,000; Full time

Job summary

Queen's University in Kingston, Ontario is offering two fully funded PhD positions in Climate Dynamics within the School of Environmental Studies. Projects focus on reduced-complexity modeling for paleoclimate and future climate change, integrating Earth system modeling and machine learning.

Applicants should have a strong quantitative background and experience in scientific programming. Funding guarantees $35,000 CAD per year for four years, plus tuition coverage and conference funding.

Qualifications

• Strong quantitative background and mathematical training.
• Experience with scientific programming and numerical methods.
• Familiarity with machine learning is desirable but not required.

Responsibilities

• Engage in interdisciplinary research on climate dynamics.
• Develop and analyze reduced-complexity dynamical models.
• Apply machine learning to Earth system modeling tasks.

Skills

Quantitative background

Scientific programming

Machine learning

Numerical modeling

Python

Tools

R

Python

Linux

Fortran

Job description

BioSciences Complex Room 3134
Kingston, Ontario, Canada
K7L 3N6
Tel [phone]
Fax [phone]
[e-mail]

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 strong 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.