Posting Title
AI for Accelerating Turbulent Flow Simulations
Location
CO - Golden
Position Type
Postdoc (Fixed Term)
Hours Per Week
40
Working at NREL
The National Renewable Energy Laboratory (NREL), located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for research and development of renewable energy and energy efficiency technologies.
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Job Description
The AI, Learning and Intelligent Systems (ALIS) group in the Computational Science Center at NREL is looking for a motivated postdoc to support machine learning research for decision support.
The postdoc should be dynamic, motivated, and have a strong interest in advancing the renewable energy mission of NREL.
The postdoc will contribute to an interdisciplinary research project that connects machine learning (ML), uncertainty quantification, and turbulent flow simulations, and that leverages high-performance computing.
AI/ML is increasingly impacting research domains across NREL, and it is deployed to alleviate the computational expense associated with complex simulations.
To fully reap the benefits of AI/ML-augmented simulations ("hybrid simulations") for decision support, we must be able to quantify the uncertainty associated with ML predictions and intelligently collect additional data to improve ML model performance.
We are looking for a postdoc with a strong technical background and broad experience in AI who is eager to develop new approaches that leverage AI and integrates them with turbulent flow simulations.
The successful candidate will collaborate with NREL staff and researchers, and other national labs to develop new approaches to enable decision support aligned with the NREL and EERE mission and goals.
Job duties include: Collaborate with NREL researchers to design and implement AI algorithms, uncertainty quantification methods for AI models, and their deployment on HPC systems.
Deploy AI developments to enhance turbulent flow simulations.
Author, present and assist in the preparation of technical papers, reports and conference proceedings on topics related to AI, uncertainty quantification, and turbulent flow simulations, and their application on HPC.
Basic Qualifications
Must be a recent PhD graduate within the last three years.
* Must meet educational requirements prior to employment start date. Additional Required Qualifications Demonstrated expertise in AI Excellent written and verbal communication skills Demonstrated experience working on high-performance computers Strong interest in NREL's mission Preferred Qualifications Demonstrated programming skills (python, C++) Experience with turbulent flow simulations Demonstrated experience in working with large multi-disciplinary teams Demonstrated experience with uncertainty quantification methods (Variational inference, MCMC, etc) Job Application Submission Window
The anticipated closing window for application submission is up to 30 days and may be extended as needed.
Annual Salary Range (based on full-time 40 hours per week)
Job Profile: Postdoctoral Researcher / Annual Salary Range: $73,200 - $120,800
NREL takes into consideration a candidate's education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees.
In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee's salary history will not be used in compensation decisions.
Benefits Summary
Benefits include medical, dental, and vision insurance; short-term disability insurance*; pension benefits*; 403(b) Employee Savings Plan with employer match*; life and accidental death and dismemberment (AD&D) insurance; personal time off (PTO) and sick leave; and paid holidays.
NREL employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component.
Some positions may be eligible for relocation expense reimbursement.
* Based on eligibility rules
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