Post Doctorate RA - Human & Earth System Science

Overview

PNNL is soliciting applications for postdoctoral scientist positions advancing machine learning/deep learning research in human-Earth system modeling and uncertainty characterization. The successful applicant will lead research at the intersection of human-Earth system modeling, Super Intelligence enabled model development, and human system modeling using the Global Change Analysis Model (GCAM; http://jgcri.github.io/gcam-doc/index.html).

Specifically, this scientist will focus on processing large ensembles of Earth system model outputs (e.g., E3SM, https://e3sm.org/; CMIP6/7 model output via ESGF https://esgf.github.io/), designing and developing machine learning/deep learning models to integrate Earth system outputs with human system models, developing scenarios to generate and analyze large ensembles of GCAM simulations, and analyzing these simulations to quantify the effect of changes in human and earth system drivers (e.g., extreme weather events) on energy, water, land systems.

The successful applicant is expected to have strong background in machine learning/deep learning methodologies and experience in applying machine learning/deep learning tools to scientific research and analysis. The scientist is also expected to lead multiple peer-reviewed publications on this work. There will be many opportunities to work with multi-disciplinary teams at PNNL, as well as collaborators in other national labs and universities.

The position aligns with and contributes to the DOE's Genesis Mission to advance a U.S. national initiative to build an SI-driven, integrated scientific platform to accelerate discovery in energy, national security, and technology. The scientist will have opportunities to contribute to this initiative by developing new tools that support scientific research and will be encouraged to explore SI-based solutions for model development consistent with DOE's Genesis Mission.

Qualifications

Minimum Qualifications:

• Candidates must have received a PhD within the past five years (60 months) or within the next 8 months from an accredited college or university.
• Background in computational science, engineering, applied mathematics, physical sciences, economics, hydrology, or related field.

Preferred Qualifications:

• Strong verbal and written communication skills
• Willingness to work both independently and within a collaborative team environment, and a proven capability to publish in peer-reviewed journals. Previous graduate-level statistical coursework, as well as substantial programming experience, are required.
• Experience with Python, R, and/or C++ is particularly useful. The applicant should have experience in and an eagerness to apply evolving Super Intelligence methodologies to all aspects of the research and modeling.

Hazardous Working Conditions/Environment

Not applicable.

Testing Designated Position

This is not a Testing Designated Position (TDP).

Rockstar Rewards

Employees and their families are offered medical insurance, dental insurance, vision insurance, robust telehealth care options, several mental health benefits, free wellness coaching, health savings account, flexible spending accounts, basic life insurance, disability insurance*, employee assistance program, business travel insurance, tuition assistance, relocation, backup childcare, legal benefits, supplemental parental bonding leave, surrogacy and adoption assistance, and fertility support. Employees are automatically enrolled in our company-funded pension plan* and may enroll in our 401 (k) savings plan with company match*. Employees may accrue up to 120 vacation hours per year and may receive ten paid holidays per year.

* Research Associates excluded.

**All benefits are dependent upon eligibility.

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Notice to Applicants

PNNL lists the full pay range for the position in the job posting. Starting pay is calculated from the minimum of the pay range and actual placement in the range is determined based on an individual's relevant job-related skills, qualifications, and experience. This approach is applicable to all positions, with the exception of positions governed by collective bargaining agreements and certain limited-term positions which have specific pay rules.

As part of our commitment to fair compensation practices, we do not ask for or consider current or past salaries in making compensation offers at hire. Instead, our compensation offers are determined by the specific requirements of the position, prevailing market trends, applicable collective bargaining agreements, pay equity for the position type, and individual qualifications and skills relevant to the performance of the position.

Minimum Salary

USD $69,000.00/Yr.
Maximum Salary

USD $119,100.00/Yr.