Water Data Science Postdoctoral Fellow

Water Data Science Postdoctoral Fellow - 91517

Organization: GO-Energy Geosciences

 

Lawrence Berkeley National Lab’s (LBNL, https://www.lbl.gov/) Earth and Environmental Sciences Area has an opening for a Water Data Science Postdoctoral Fellow to join the team.

 

In this exciting role, you will investigate the impacts of hydrological disturbances, such as floods or droughts, on water quality in different river corridors of the United States. You will work within a five-year project funded by the Department of Energy’s Environmental Systems Science program to understand and predict watershed resilience to streamflow perturbations caused by extreme climate events, changes in land use, water management, or other disturbances.

 

You will be responsible for integrating large temporal and spatial watershed datasets, conducting analysis using statistical, wavelet, and pattern recognition techniques, and implementing machine-learning models to predict stream water quality at local to regional scales. You will also involve building a data-driven framework that will enable reproducible generation and analysis of integrated watershed datasets. Solutions developed within the postdoctoral project will ultimately enable better predictions of water quality responses to extreme hydrological perturbations.

 

What You Will Do:

• Develop and use data-driven models to understand river water quality response and resilience to hydrological perturbations at multiple scales, from reach to watershed to regional scales.

• Integrate and analyze complex datasets including high-resolution climate and spatial watershed datasets with measurements of river discharge, water quality.

• Research and develop approaches for machine learning-based modeling of water quality and extracting patterns in the data using statistical, wavelet, classification, or other data mining methods.

• Contribute to building a Python-based framework for water quality data integration, analysis and prediction.

• Author peer-reviewed conference or journal papers, and contribute to grant proposals.

 

What is Required:

• Ph.D. in Environmental Sciences/Engineering or other related technical disciplines.

• Prior experience with data analysis, modeling, machine learning, or numerical optimization for watershed hydrology or biogeochemistry.

• Theoretical understanding and application of data analysis methods such as statistical techniques, signal processing, pattern recognition, or data-informed mathematical modeling.

• Excellent written and oral communication skills, with an established record of peer-reviewed publications

• Commitment to open science, open data, and implementing maintainable and reusable software/data products for broader scientific use.

 

Desired Qualifications:

• Experience working with datasets used in watershed or water quality modeling (e.g. NHDPlus, climate drivers, stream temperature/salinity/dissolved oxygen).

• Prior analyses or modeling of extreme events such as floods/droughts.

• Experience with using traditional and deep machine learning methods such as methods for time series analysis, classification, and prediction.

• Familiarity with libraries, frameworks, or workflow tools that enable data analytics and machine learning (e.g., NumPy, Pandas, Scikit-learn, Keras, Tensorflow, Jupyter Notebooks).

• Programming experience with Python (preferred) or R.

 

To be considered, please include the following to your online applications:

• Cover Letter: Include a cover letter introducing yourself, your application, and describing your interest in the position.

• Curriculum Vitae/Resume: Either an academic CV or a resume is acceptable.  Be sure to highlight technical skills, interests, and synergistic activities relevant to the position. Include links to software projects or public code repositories.

• 3 References: Provide contact information for three professional references with whom we may communicate regarding your work and your application.

 

For full consideration, please apply by December 17, 2020.

 

Notes:

• This is a full-time, 2 year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.

• This position is represented by a union for collective bargaining purposes.

• Salary will be predetermined based on postdoctoral step rates.

• 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.

• Diversity, equity, and inclusion are core values at Berkeley Lab. Our excellence can only be fully realized by faculty, students, and staff who share our commitment to these values. Successful candidates for our faculty positions will demonstrate evidence of a commitment to advancing equity and inclusion.

• Work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.

 

How To Apply

Apply directly online at http://50.73.55.13/counter.php?id=189615 and follow the on-line instructions to complete the application process.

 

Equal Employment Opportunity: Berkeley Lab is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. Berkeley Lab is in compliance with the Pay Transparency Nondiscrimination Provision under 41 CFR 60-1.4. Click here (https://www.dol.gov/agencies/ofccp/posters) to view the poster and supplement: "Equal Employment Opportunity is the Law."

 

Lawrence Berkeley National Laboratory encourages applications from women, minorities, veterans, and other underrepresented groups presently considering scientific research careers.