Scientist III

University Corporation for Atmospheric Research•Boulder, CO

52d•Hybrid

About The Position

We seek a creative and highly motivated scientist to conduct innovative research that integrates machine learning (ML), artificial intelligence (AI), and advanced data assimilation techniques into next-generation analysis and forecasting systems for numerical weather prediction. This position will focus on developing and evaluating novel approaches that enhance the assimilation of remote sensing products, such as all-sky radiances and aerosol retrievals, within the NSF NCAR’s community coupled data assimilation (DA) systems such as MPAS-GOCART2G-JEDI, MPAS-DART, or WRF-Chem/WRFDA in the Mesoscale and Microscale Meteorology (MMM) laboratory. The scientist will be encouraged to explore and prototype new ML/AI approaches, even without extensive prior AI experience, provided they have a strong foundation in numerical modeling, data processing, or data assimilation theory. Example research areas include, but are not limited to, ML-based observation pre- or post-processing, surrogate observation operators, adaptive quality control (QC) or error modeling, efficient handling of large satellite data pipelines, and hybrid ML-DA methods to improve analysis accuracy and computational efficiency (e.g., multi-scale localization and/or inflation). The scientist will also contribute to advancing our coupled DA systems and end-to-end cycling workflow within the DA framework. As part of team efforts, activities may include incorporating ML/AI components into existing DA infrastructure, integrating high-volume remote sensing datasets, evaluating novel algorithms within cycling experiments, and developing tools that enhance system scalability and scientific impact. The scientist will work both independently and collaboratively in a multidisciplinary research environment, developing and implementing research plans, analyzing results with rigorous scientific interpretation, and publishing findings in peer-reviewed journals. This position offers strong support for innovative exploration and provides opportunities to shape the future of satellite DA and AI-enabled environmental prediction.

Requirements

Ph.D. degree within the last 5 years or expected within the next 6 months in atmospheric science, computer science, statistics, or a related area.
Has solid knowledge in a relevant scientific field, with an ability to apply theories, methods, or frameworks to complex or interdisciplinary problems.
Is familiar with project planning and proposal development, including scoping technical work, aligning with program goals, and supporting deliverable management.
Is skilled in developing or refining scientific tools, workflows, or models, including quality assurance and integration with broader project efforts.
Is skilled in using data analysis tools used for data preparation, visualization, or workflow automation.
Is committed to mentoring with the ability to guide less experienced colleagues on methods, tools, and collaborative practices.
Is capable of contributing to peer-reviewed publications, sponsor reports, or presentations, with clear and accurate scientific communication tailored to varied audiences.
Collaborates effectively within multidisciplinary teams, integrating diverse inputs and fostering productive working relationships.
Demonstrates the ability to design and carry out analyses or investigative tasks independently using established methods, adapting approaches as needed.
Understanding of inverse modeling.
Demonstrated skill in scripting languages (e.g., python/shell), especially related to tests on supercomputers.
Understanding of satellite meteorology or retrievals
Demonstrated ability to work independently and collaboratively as part of a research team.
Excellent time management and organization skills.
Demonstrated and effective written and oral communication skills.
Knowledge of Earth system modeling.

Nice To Haves

Demonstrated knowledge of assimilating remote sensing data.
Knowledge of data assimilation or ML/AI algorithms.
Experience with ML/AI approaches for Earth system modeling.
Experience with data assimilation and cycling experiments.

Responsibilities

Conduct innovative research exploring ML/AI approaches for satellite data assimilation, including ML-based data processing, QC, or bias/error modeling.
Integrate novel methods to existing coupled DA systems in collaboration with system developers.
Design and conduct cycling experiments, analyze results with sound scientific interpretation, and evaluate ML/AI enhancements on Earth system prediction, contributing to the enhancement of community coupled systems.
Prepare research findings for presentation at conferences and for publication in peer-reviewed journals.
Assist with the preparation of reports, scientific proposals, and documentation, as needed.
Contributes to papers, reports, technical documentation, data sets, findings, and/or proposals.
Mentors less experienced colleagues, collaborates across disciplines, and supports proposal development to advance scientific goals through high-quality, methodologically sound work.
Engages in mission-aligned activities both within the organization and throughout the broader scientific community.
Contributes through a combination of scientific expertise, professional service, and education and outreach efforts.
Designs and executes scientific analyses; develops, and adapts, and/or tests hypotheses, models and/or tools.
Contributes to papers, reports, technical documentation, data sets, findings, and/or proposals.
Mentors less experienced colleagues, collaborates across disciplines, and supports proposal development to advance scientific goals through high-quality, methodologically sound work.
Engages in mission-aligned activities both within the organization and throughout the broader scientific community.
Contributes through a combination of scientific expertise, professional service, and education and outreach efforts.
Designing and executing scientific investigations or analyses using established and/or emerging methods to address defined research, operational, or applied science questions.
Developing or adapting tools, models, or methods to support data collection, processing, analysis, system evaluation, or experimental workflows.
Performing detailed analysis and interpretation of observational, experimental, or model-generated data, contributing to new insights or improvements in scientific understanding.
Leading or contributing to components of a larger project or small project teams through coordination of activities, deliverables, and technical direction across collaborators.
Authoring or co-authoring peer-reviewed publications, technical documentation, data sets, findings, project reports, and/or proposals, and presenting findings in internal, sponsor, or scientific community settings.
Collaborating across disciplines and institutions, contributing technical expertise to team-based projects and enhancing interdisciplinary research or operational outcomes.
Supporting proposal development and project planning activities by contributing to scoping, technical writing, and alignment with programmatic objectives.
Providing technical guidance to less experienced staff/students, mentoring on methods, tools, and best practices while engaging in and modeling inclusive team participation.
Developing products, tools, instruments and/or technologies for projects and programs.

Benefits

UCAR affirms its commitment to employees through competitive benefits . In addition to medical, dental, vision, retirement, and life insurance, UCAR offers a variety of programs focused on work-life balance and professional, and personal development.
These include: Tuition Assistance, time off allowance to attend classes, and other professional development opportunities.
UCAR contributes 10% of your eligible pay into your retirement account; 100% fully vested on day one.
Starting minimum accrual of 20 days of personal time off each year (prorated for less than full-time positions).
10 paid holidays.
12 weeks of paid parental leave.
Short-term medical leave paid at 100% of your regular salary.
EcoPass for local Colorado residents to use the Denver and Boulder-area transit system at no cost.

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