Post Doctorate RA C
Full/Part Time: Full-Time
The candidate for this position will develop and apply machine learning and deep learning algorithms for spatiotemporal data analysis, hydrogeological characterization and mapping, geophysical data interpretation, flow and transport model optimization, and/or environmental monitoring network design. The primary scope of this position includes harvesting, management, processing, and analysis of environmental data associated with subsurface soil and groundwater characterization, remediation, and monitoring. The incumbent should also prepare manuscripts for project reports, patents, and publications and present work internally and externally at national/ international conferences, and develop new research ideas independently.
Equal Employment Opportunity
Battelle Memorial Institute (BMI) at Pacific Northwest National Laboratory (PNNL) is an Affirmative Action/Equal Opportunity Employer and supports diversity in the workplace. All employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, marital or family status, sexual orientation, gender identity, or genetic information. All BMI staff must be able to demonstrate the legal right to work in the United States. BMI is an E-Verify employer. Learn more at jobs.pnnl.gov.
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.
Candidates must have received a PhD in Machine Learning, Geostatistics, Civil and Environmental Engineering, or a related field.
The candidate must have knowledge and experience in developing and applying machine learning and deep learning techniques, preferably in the areas of environmental systems. The incumbent should have good programming skills, be proficient in at least one programming language (R, Python, Matlab, Java, or C++), and have experience with analysis and visualization tools (e.g., Matlab, Tecplot, ArcGIS, Photoshop, or AutoCAD). The successful candidate should have good written and oral communication skills and ability to work independently and collaborate effectively in a teaming environment. Familiarity with linux system, high performance computing, and flow and transport modeling are also preferred.
3.5 GPA or higher is preferred.
Organization and Job ID
Job ID: 308018
Directorate: Energy and Environment
Division: Earth Systems Science