University of Chicago MS in Environmental Science
Postdoctoral Scholar - ML Emulator For Data Assimilation
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Postdoctoral Scholar - ML Emulator For Data Assimilation
Postdoctoral Scholar - ML Emulator For Data Assimilation
The University of Chicago’s Department of Geophysical Sciences and AI for Climate Initiative (AICE) invite applications for a postdoctoral researcher to work with Prof Pedram Hassanzadeh on the interface of data assimilation and machine learning (ML) for global state analysis of the ocean and atmosphere, with a focus on biogeochemical cycles.
About the Research
This position involves developing:
- An ML-based atmosphere-ocean coupled emulator using high-resolution simulation data.
- A data assimilation framework accelerated by the emulator, initially testing with idealized regional cases before scaling to global applications.
- Hands-on contributions to the InMOS project, a multi-institution international collaboration led by NYU’s Prof Laure Zanna and Princeton’s Prof Laure Resplandy.
Project Goals
- Build a global synthesis of oceanic carbon, oxygen, and heat cycling dynamics since pre-industrial times.
- Quantify critical ocean fluxes (e.g., acidification, warming, and deoxygenation) and their drivers.
Position Details
- Duration: Full-time appointment for one year, extendable up to three years (subject to performance and funding).
- Eligibility for Renewal: Outstanding applicants with >3 years postdoc experience considered for research scientist appointments.
- Benefits: Eligible for University benefits (via Garnett-Powers).
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Collaborative Networks
- Integration within:
- InMOS consortium
- AICE & OBVI (Ocean Biogeochemistry Virtual Institute)
- UChicago initiatives (Data Science Institute, Climate and Sustainable Growth Institute, AI+Science Initiative).
Responsibilities
- Develop ML data emulators for high-dimensional climate system components.
- Implement data assimilation systems (including emulator-driven techniques).
- Collaborate with InMOS team (e.g., lead PIs at NYU & Princeton) to ensure alignment.
- Adapt test cases from regional to global scales via observational data integration.
- Publish findings in peer-reviewed journals and present at conferences.
Required Qualifications
Applicants must meet the following criteria:
- PhD (confirmed at appointment) in:
- Climate science
- Applied mathematics/physics
- Engineering/physical oceanography
- Computational science
- or related geo 빙 알 수정Fisher.provider 툴고지의 browse
- Technical skills:
- Proficiency in programming/numerical computing (e.g., Python, Fortran, C).
- Experience with high-performance computing and large-scale data analysis.
- Collaborative adaptability: Ability to work independently and within multi-disciplinary teams.
- Strong communication skills (oral/written).
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Preferred Qualifications
Experience with:
- Machine learning (especially deep neural networks).
- Data assimilation techniques (e.g., ensemble Kalman filtering, Markov Chain Monte Carlo).
Application Process
To apply, please email pedramh@uchicago.edu (subject line: "Application: InMOS") with a single PDF containing:
- Curriculum vitae (with publications list).
- Reference contact information (minimum 3 contacts).
- 1-page statement outlining:
- Relevant past research.
- Technical expertise.
- Alignment with the **po ロする would have did.
Deadline & Openness
- Review starts immediately; applications open until June 1, 2025.
- Early submissions encouraged.
Commitment to Inclusion
The University of Chicago is an Equal Employment/Affirmative Action Employer and welcomes applicants from historically excluded groups. Reasonable accommodations for applicants can be requested via: 📞 773-834-3988 or � contribution levelContactasis emailser by all 그랬(comment.ms)! 긱(g('%weights())):
Contact Details
University of Chicago Department of the Geophysical Sciences 5734 S. Ellis Avenue 📞 (773) 702-8101 🌐 geosci@uchicago.edu
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