Colorado State University 20-118: Data Assimilation and Machine Learning Scientist in Fort Collins, Colorado
Working Title 20-118: Data Assimilation and Machine Learning Scientist
Position Location Fort Collins, CO
Research Professional Position Yes
Posting Number 202000732AP
Position Type Admin Professional/ Research Professional
Classification Title Postdoctoral Fellows
Number of Vacancies
Work Hours/Week 40
Proposed Annual Salary Range $55,000
Desired Start Date
Position End Date (if temporary)
To ensure full consideration, applications must be received by 11:59pm (MT) on 10/25/2020
Description of Work Unit
The Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University (CSU) is a multi-million dollar research organization located on CSU’s Foothills Campus in Fort Collins, Colorado. CIRA is a cooperative institute (CI) that is also a research department within CSU’s College of Engineering, in partnership with the Department of Atmospheric Science. Its vision is to conduct interdisciplinary research in the atmospheric sciences by entraining skills beyond the meteorological disciplines, exploiting advances in engineering and computer science, facilitating transitional activity between pure and applied research, leveraging both national and international resources and partnerships, and assisting the National Oceanic and Atmospheric Administration (NOAA), CSU, the State of Colorado, and the Nation through the application of our research to areas of societal benefit. Expanding on this Vision, our Mission is to serve as a nexus for multi-disciplinary cooperation among CI and NOAA research scientists, University faculty, staff and students in the context of NOAA-specified research theme areas in satellite applications for weather/climate forecasting. Important bridging elements of the CI include the communication of research findings to the international scientific community, transition of applications and capabilities to NOAA operational users, education and training programs for operational user proficiency, outreach programs to K-12 education and the general public for environmental literacy, and understanding and quantifying the societal impacts of NOAA research.
The Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University seeks to fill a postdoctoral fellowship in November 2020 as part of a National Science Foundation (NSF) award to train a new scientist in data assimilation and machine learning techniques. Located at CIRA in Fort Collins, Colorado, this fellowship may last up to 3 years contingent upon NSF funding availability.
Recent work at CIRA has focused on non-Gaussian-based data assimilation systems that are mixed Gaussian-lognormal based. As part of a previous award, a second non-Gaussian distribution has been detected in the Lorenz 63 model, as well as early indication of this reverse lognormal distribution in the output from the Weather Research and Forecasting (WRF) model. The individual in this position will develop the theory of the reverse lognormal in both variational and ensemble data assimilation systems.
Specifically, the individual in this position will serve as a member of the CIRA data assimilation group and will test the robustness of machine learning techniques to identify the links between non-Gaussian distributions and different atmospheric scale dynamics, convert the hybrid version of WRF-GSI to have a non-Gaussian component, and assess the robustness of new non-Gaussian based ensemble systems along with advancing the development of a new version of the Maximum Likelihood Ensemble Smoother.
Required Job Qualifications
Please specifically address all required qualifications in your cover letter. • PhD in Physics, Mathematics, Statistics, Meteorology, or related physical science field; • proficiency in programming in Fortran90 or higher; • working knowledge of machine learning techniques or data assimilation systems; • higher education in fundamental mathematics and/or physics.
Preferred Job Qualifications
Please specifically address all applicable preferred qualifications in your cover letter. • solid quantitative educational background in fundamental Mathematics, Physics, and/or Statistics; • proficiency with scripting in a Linux environment; • familiarity with high performance computing; • working knowledge of variational data assimilation; • knowledge of WRF-GSI or other numerical weather/ocean prediction systems; • knowledge of Bayesian Theory; • knowledge of the mathematical field of Numerical Analysis, i.e. preconditioning, numerical linear algebra, NSDE etc.; • knowledge of mesoscale and/or synoptic meteorology; • proficiency in MATLAB or equivalent analysis and display software.
Reflecting departmental and institutional values, candidates are expected to have the ability to advance the Department’s commitment to diversity and inclusion.
Job Duty Category Machine Learning to Identify Non-Gaussian Dynamics
• assess the robustness of machine learning methods including Support Vector Machines (SVM) to detect changes in distribution with the series of more complex Lorenz models; • assess and determine the reliability of machine learning methods including SVM in different dynamical situations with the WRF model; • determine the impacts and reliability of using machine learning methods including SVM to direct the WRF-GSI to switch distributions.
Percentage Of Time 40
Job Duty Category Non-Gaussian Hybrid WRF-GSI
• convert the Gridpoint Statistical Interpolation (GSI) system and the hybrid system to allow for a lognormal component (e.g., for the moisture variable) and a version with the logarithmic transform approach, as well as developing the new reverse lognormal theory; • test and develop the new non-Gaussian versions of the Maximum Likelihood Ensemble Filter; • help develop and test the robustness of the new Maximum Likelihood Ensemble Smoother as an alternative to the ensemble component of the hybrid GSI; • test and compare the different versions of the WRF-GSI against different distributed error scenarios; • conduct WRF-GSI experiments to assess the impacts on short and medium range forecasts from the different configurations of the hybrid WRF-GSI system.
Percentage Of Time 40
Job Duty Category Documentation & Reporting
• prepare manuscripts for submission to peer-reviewed journals and edit through the review process; • prepare and present conference abstracts, posters, and/or presentations; • present research results during domestic and international conferences; travel may be required.
Percentage Of Time 20
Special Instructions to Applicants
References will not be contacted without prior notification of candidates.
In your cover letter, please specifically address the required and preferred qualifications of this position. A cover letter that fails to address the required and preferred qualifications of this position may not be further considered after review by the search committee.
Conditions of Employment Pre-employment Criminal Background Check (required for new hires)
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The Section 504 and ADA Coordinator is the Vice President for Equity, Equal Opportunity, and Title IX, 101 Student Services Building, Fort Collins, CO 80523-0160, (970) 491-5836.
Background Check Policy Statement
Colorado State University strives to provide a safe study, work, and living environment for its faculty, staff, volunteers and students. To support this environment and comply with applicable laws and regulations, CSU conducts background checks. The type of background check conducted varies by position and can include, but is not limited to, criminal history, sex offender registry, motor vehicle history, financial history, and/or education verification. Background checks will also be conducted when required by law or contract and when, in the discretion of the University, it is reasonable and prudent to do so.
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Maximum Requested 3
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