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We are seeking up to two climate scientists passionate about understanding the rapidly changing Arctic system, with emphasis on sea-ice modeling and polar extreme events. The ideal candidate combines strong analytical and computational skills with curiosity and creativity. They will draw on both physical understanding and data-driven methods, including AI and machine learning, to uncover new patterns in complex Arctic/Antarctic datasets. The role is inherently cross-disciplinary, spanning oceanography, sea ice and polar dynamics, climate extremes, and operational research.
Job Responsibility:
Applying data analysis and AIML methods to develop an Arctic ship weather routing model, using inputs from observations, dynamic climate models, and extreme events analysis
Investigating the interplay between sea-ice loss, ocean circulation, and extreme events, and their implications for climate feedbacks
Collaborating with modeling teams to design and interpret targeted experiments (e.g., wave–ice–ocean coupling or idealized scenarios under reduced sea-ice extent)
Synthesizing results into concise scientific products, reports, and working papers supporting future proposals and collaborations
Contributing to the integration of Arctic process understanding into broader CMCC initiatives on emissions, extremes, and carbon-cycle modeling
Engaging with internal and external partners, including Arctic research institutes and observation networks, as well as shipowners and other maritime stakeholders, to strengthen data-sharing and methodological innovation
Requirements:
PhD in Mathematics, Physics, Computer Science, Engineering or Earth Sciences
Demonstrated experience in Arctic and Antarctic research, with emphasis on polar oceans and polar climate dynamics, sea ice variability, polar extreme events
Advanced programming skills in Python and experience with scientific and visualization libraries (e.g., xarray, pandas, Plotly, Matplotlib)
Excellent written and spoken English and strong communication skills across scientific and multidisciplinary teams
Nice to have:
At least two recommendation letters
Collaborative mindset, intellectual curiosity, and commitment to advancing climate science