- Req. Number: IRC113248
- Organization: EES-14/Earth System Observations
- City, State: Los Alamos, New Mexico
- Recruiter Name: Sutton, Jennifer Anne
- Recruiter Email: [email protected]
What You Will Do
Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in science and engineering on behalf of national security. You will work in the Earth and Environmental Sciences Division, and will be primarily engaged with the Atmosphere, Climate, and Ecosystem Sciences and Computational Earth Science Teams. Team members will support and enrich your research and professional goals through their outstanding breadth and depth of expertise in their respective fields.
We seek someone interested in developing and applying machine learning techniques to pressing problems related to water security. In particular, your research will include:
- Building machine learning models (e.g. LSTMs, Transformers) for streamflow and flood prediction
- Incorporating novel data sources to capture human impacts on watersheds and directly to rivers into your models
- Exploring techniques for building models that incorporate known physical principles
- Developing and deploying methods for understanding what your models learned
This position provides an opportunity to apply your skills toward a variety of impactful problems. Although your project will focus on streamflow prediction, domain-specific knowledge (e.g. hydrology, climate and/or earth sciences) is not required. Depending on your interests and time, you may have opportunities to contribute to other projects as well, including for example modeling mosquito-borne diseases, mapping permafrost presence with ML, or estimating water quality from remotely-sensed images. Our teams will provide context, background, and guidance as you familiarize yourself with the domain-specific applications. While the overall research goals for these projects have been established, there is significant flexibility in the way these goals can be achieved, and novel approaches are encouraged.
What You Need
Minimum Job Requirements:
• Familiarity with machine learning/deep learning models
• Demonstrated/demonstrable proficiency with Python
• Experience with data wrangling and curation
• An ability to work and communicate effectively in a diverse team environment
Education/Experience: An MS or in computer science, data science, mathematics, climate science, earth science, or closely related field, and completed within the past three years, and have not yet been accepted into another master’s program or Ph.D. program.
• Familiarity with PyTorch, TensorFlow, and/or sequential ML modeling
• Domain-specific experience, such as hydrological modeling or streamflow routing
• Publication in refereed journals and a history of successful research in collaborative team environments
• Experience with geospatial data, Google Earth Engine, and/or PostGIS
Location: This position will be located in Los Alamos, NM.
The COVID vaccine is mandatory for all Laboratory students working on-site and students coming on-site, as well as any students coming on-site for any reason; unless granted an accommodation under applicable state or federal law.
Note to Applicants:
Applicants must submit their materials through LANL’s job portal: http://jobs.lanl.gov. In addition, we ask that you also email your CV and a cover letter expressing your interest and how you meet the requirements of the position to [email protected] . The optimal start date for this position is October 3, 2022, but we maintain flexibility here. This position requires relocation to Los Alamos or surrounding areas.
Where You Will Work
The Earth and Environmental Sciences (EES) Division is the intellectual home of Earth, atmosphere, hydrology, and ecology disciplines at the Laboratory. Fundamental discovery science and mission-focused applications unravel poorly understood processes and support decisions of national importance.
Located in northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security. LANL enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns. Our generous benefits package includes:
§ PPO or High Deductible medical insurance with the same large nationwide network
§ Dental and vision insurance
§ Free basic life and disability insurance
§ Paid childbirth and parental leave
§ Award-winning 401(k) (6% matching plus 3.5% annually)
§ Learning opportunities and tuition assistance
§ Flexible schedules and time off (paid sick, vacation, and holidays)
§ Onsite gyms and wellness programs
§ Extensive relocation packages (outside a 50 mile radius)
Directive 206.2 – Employment with Triad requires a favorable decision by NNSA indicating employee is suitable under NNSA Supplemental Directive 206.2. Please note that this requirement applies only to citizens of the United States. Foreign nationals are subject to a similar requirement under DOE Order 142.3A.
No Clearance: Position does not require a security clearance. Selected candidates will be subject to drug testing and other pre-employment background checks.
New-Employment Drug Test: The Laboratory requires successful applicants to complete a new-employment drug test and maintains a substance abuse policy that includes random drug testing. Although New Mexico and other states have legalized the use of marijuana, use and possession of marijuana remain illegal under federal law. A positive drug test for marijuana will result in termination of employment, even if the use was pre-offer.
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