Postdoctoral Research Fellow
This is a term appointment (renewable) as a federal government employee with a comprehensive benefits package. As a federal employee, you and your family will have access to a range of benefits that are designed to make your federal career very rewarding. Learn more about federal benefits https://www.usajobs.gov/Help/working-in-government/benefits/ or www.opm.gov
The Applied Physics Branch at the US Army Research Laboratory (ARL) is seeking postdoctoral research fellows to conduct research and development in one of the following areas: Machine Learning, Artificial Intelligence or Uncertainty Quantification to support the next-generation combat vehicles, one of the Army top six modernization priorities. This fellowship will provide an opportunity for the award recipients to pursue independent research that supports the mission of ARL. The Fellows will benefit by working alongside some of the Nation's best scientists and engineers.
Major Job Duties and Responsibilities:
- Be a part of a team for development of System Survivability Engineering Software (SSES), aiming to provide predictive solutions for vehicle active protection technologies in support of next-generation combat vehicles.
- Research and develop techniques/methodology in uncertainty quantification, model verification and validation for the SSES software.
- Research and develop artificial intelligence/machine-learning algorithms to be applied for potential vehicle protection technologies, such as threat identification, fire controls for multiple engagements, and cooperative protection network, etc.
- Assist in gathering and structuring SSES Big Data, which consists of an array of warehouses in threats, sensors, countermeasures, vehicles, environments, experimental data, simulation results, etc.
- Present research in professional conferences.
- Publish technical reports and/or journal papers.
- Must be an US citizen and be able to obtain a security clearance.
- By the time of the appointment, applicants must have completed all the requirements for a Ph.D. or Sc.D. degree in the physical sciences, engineering, computational sciences, applied math, statistics or other related fields.
- Applicants must be within five years from date of award of Ph.D. or Sc.D. by the appointment time.
- Applicants must demonstrate exceptional qualifications with respect to academic and scholarly achievement, as evidenced by research and publication.
- Candidates are expected to have conducted research on a major scientific or engineering problem during their thesis work or have provided a new approach or insight evidenced by a recognized impact in their field.
Skills and Competencies:
- Knowledge of data uncertainty, model uncertainty and uncertainty aggregation and reduction methods, including forward uncertainty propagation and inverse uncertainty quantification problems.
- Knowledge of machine learning/artificial intelligence methods/techniques: Bayesian theorem and network, neural network, dimensionality reduction, clustering, nonparametric methods, Markov models, reinforcement learning, etc.
- Proficient in programming languages, C++ preferred.
- Experience in the use of Git version control tool preferred.
- Experience in the use of high performance computing systems preferred.
- Curriculum vitae
- Statement of previous research, describing research accomplishments and impact
- Unofficial copies of transcripts (undergraduate and graduate)
The positions are open immediately until filled. Finalists will be asked to present a seminar at the Army Research Laboratory.
Dr. Michael Chen
Applied Physics Branch
US Army Research Laboratory