Job Title: Computational Hydro-Geochemistry Postdoctoral Researcher
Location: Los Alamos, NM, US
Organization Name: EES-14/Earth System Observations
Minimum Salary:
Maximum Salary:
What You Will Do
The Earth System Observations Group (EES-14) in the Earth and Environmental Sciences (EES) Division at Los Alamos National Laboratory (LANL) is seeking applications from diverse candidates with expertise in surface and groundwater hydrology, geochemistry, ecohydrology, or hydrodynamics (preferably related to coastal systems). Research experience on salinity in coastal systems, nutrient and water quality, sediment transport, and hydrological feedbacks with vegetation are desirable. This position will focus on using numerical models to better understand coastal surface and subsurface hydrologic processes and nutrient transport under climate change. The successful candidate will work with a multi-disciplinary team of hydrologists, computational geoscientists, geomorphologists, ecologists, and climate modelers focused on developing multi-physics models for high-resolution simulations using high-performance computational architectures. The successful candidate will have the opportunity to work with the current state-of-the-art DOE-sponsored modeling codes such as the Advance Terrestrial Simulator (ATS) and evaluate model predictions with observations from field or remote sensing.
What You Need
Minimum Job Requirements:
- Research background in one or more of the following: watershed hydrology, coastal hydrology, groundwater hydrology, sediment transport.
- Experience in surface and subsurface hydrologic modeling and/or computational geosciences.
- Demonstrated research ability, as evidenced by publications in refereed journals and a history of successful research in collaborative team environments
Education: A Ph.D. in hydrology, ecology, environmental science, geosciences, civil engineering, water resources engineering, climate modeling or a closely related field. The candidate must have completed all Ph.D. requirements by commencement of the appointment or received a Ph.D. within the last five years.
Desired Qualifications:
- Experience in developing and/or using physics-based models to predict and understand coastal hydrological interaction.
- Experience in modeling coastal freshwater and saltwater interaction
- Experience in using GIS and remote sensing software to process vector and raster files
- Experience with Python and one of the following: Fortran, C, or C++
- Experience with using version control tools (e.g., git)
- Experience and knowledge of computational physics and/or climate modeling, construction and application of physical models, the analysis and incorporation of data and observations for model development and testing; and experience on high performance computing
Location: This position will be located in Los Alamos, NM.
COVID Vaccine:
The COVID vaccine is mandatory for all Laboratory employees, on-site contractors, and on-site subcontractors unless granted an accommodation under applicable state or federal law. This requirement will apply to those working on-site, those teleworking, and all new hires.
Note to Applicants:
For full consideration, please submit a cover letter describing background and relevant experience, a CV, and (C) contact information for three professional references. This appointment is valid for two years with a possible third year extension. Note that US citizenship is not a requirement.
Review of applications will begin on December 5th 2022 and until the position is filled.
For additional information, please contact Yu Zhang (yuzhang@lanl.gov).
Where You Will Work
Located in beautiful northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security. 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)
Additional Details
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.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.
Equal Opportunity: Los Alamos National Laboratory is an equal opportunity employer and supports a diverse and inclusive workforce. All employment practices are based on qualification and merit, without regard to race, color, national origin, ancestry, religion, age, sex, gender identity, sexual orientation or preference, marital status or spousal affiliation, physical or mental disability, medical conditions, pregnancy, status as a protected veteran, genetic information, or citizenship within the limits imposed by federal laws and regulations. The Laboratory is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. To request such an accommodation, please send an email to applyhelp@lanl.gov or call 1-505-665-4444 option 1.
