Job Description

In this exciting role, you will work in the Climate and Ecosystem Sciences Division at Lawrence Berkeley National Laboratory (Berkeley Lab) developing statistical methods to enhance the understanding and predictability of extreme weather and climate processes. The research will connect the underlying physics of atmospheric processes to impactful weather events and quantify the uncertainty among different physical drivers. This work aims to clarify how natural variability and external forces interact using observations and climate model outputs. Expertise in statistical modeling, Bayesian analysis, machine learning, high-dimensional statistics, and computational statistics is essential. You will collaborate with interdisciplinary teams of physical scientists, climate scientists, and computer scientists to advance novel statistical methods in climate science.

What You Will Do:

• Develop statistical methodologies for dynamic processes, quantifying natural variability, external forcing, and event attribution. Apply these methods to observational and climate model data to yield insights into extreme weather and climate.
• Contribute to proposals aligned with Berkeley Lab's science mission.
• Author technical reports and peer-reviewed articles, and communicate findings to collaborators, peers, and the media.
• Collaborate within interdisciplinary research teams.

What is Required:

• Ph.D. in statistics, applied mathematics, or computer science or a closely related field, or equivalent experience.
• Two to five years of relevant experience, beyond the highest customary degree in a related field.
• Expertise in statistical modeling of physical processes.
• Expertise modeling climate data, from climate model output and/or observations, to gain insight into atmospheric processes and producing uncertainty estimates about the conclusions.
• Demonstrated expertise with high-performance computing and large datasets.
• Proficiency with one or more programming languages (e.g., Julia, R, Python, Matlab, C/C++, etc.).
• Demonstrated ability producing high-quality visualizations of complex data/results.
• Established publication record in peer-reviewed journals.
• Excellent oral and written communication skills for both technical and non-technical settings.
• Ability to work independently and collaborate with interdisciplinary teams.

Notes:

• This is a full time, 5 years, career-track term appointment that may be converted to career based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs. A Career position will be considered for those who have relevant experience or for existing Career-Track or Term staff who have already exhausted the number of years eligible to hold a Career-Track role.
• This full salary range of this position is between $91,484 to $ 219,562 per year and is expected to pay between a targeted range of $121,983 to $170,766 per year depending upon candidates' full skills, knowledge, and abilities, including education, certifications, and years of experience.
• This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
• Work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.

Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov

Berkeley Lab is committed to inclusion, diversity, equity and accessibility and strives to continue building community with these shared values and commitments. Berkeley Lab is an Equal Opportunity and Affirmative Action Employer. We heartily welcome applications from women, minorities, veterans, and all who would contribute to the Lab's mission of leading scientific discovery, inclusion, and professionalism. In support of our diverse global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.

Equal Opportunity and IDEA Information Links:
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Job Tags

Full time,

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