Research Scientist, Systems and Decision Sciences

Requisition Id 16128 

 

­­Overview:  

The Systems and Decision Sciences (SDS) Group at Oak Ridge National Laboratory is seeking a highly motivated scientist to contribute to research in the areas of complex systems in support of the scientific mission of the Laboratory and of the Department of Energy. The role expects collaborations with scientists in interdisciplinary research projects within the Oak Ridge National Laboratory.

 

The SDS Group consists of researchers focused on delivering computational solutions for the study of complex engineered systems using scalable architecture-aware algorithms coupled with artificial intelligence, applied mathematics and statistical sciences with an emphasis on applications relevant to the DOE mission space.

 

This position is within the Mathematics in Computation (MiC) Section in the Computer Science and Mathematics (CSM) Division. The MiC Section creates the mathematics and algorithms that harness machines, ideas, and data, to enable far reaching scientific breakthroughs. The CSM Division delivers fundamental and applied research capabilities in a wide range of areas, including applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and visualization technologies, programming systems and environments, and system science and engineering. This position seeks a candidate with demonstrated experience in applied machine learning and computational data science for complex, safety-critical systems, with an emphasis on time-series anomaly detection, multivariate signal analysis and statistical evaluation methodology for cyber-physical security applications.

 

Major Duties/Responsibilities:

Responsibilities for this position include working closely with a project team to create, evaluate, and publish novel research ideas, supervise and mentor students, contribute to the grant-writing and securing process, and collaborating with our partners and sponsors. Research activities include, but are not limited to:

• Formulating research initiatives that push the frontiers in the study of complex engineered systems.
• Publishing papers in high-quality refereed conferences and journals.
• Actively collaborating with industry, academia, government labs, and applications developers in a variety of venues.
• Contributing to research proposals and reporting to major funding agencies.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. 

 

Basic Qualifications

• Requires a Ph.D. in Computer Science, Computational Sciences, Mathematics or a related discipline.
• A minimum of two years of relevant research experience, beyond the Ph.D.
• An established track record of research in an area relevant to one or more areas of expertise of the group.
• Demonstrated impact in R&D projects via high-quality peer-reviewed publications, codebases, curated datasets, etc.

 

Preferred Qualifications

• A minimum of five years of relevant experience, beyond Ph.D.
• Demonstrated research experience in the applications of graph-theoretic analysis, probabilistic approaches to complex systems, time-series analysis and multivariate signal analysis.
• Excellent written and oral communication skills.
• Effective interpersonal skills.

 

Visa sponsorship is not available for this position.

 

For questions regarding applying to this position, please email ORNLRecruiting@ornl.gov indicating the job requisition ID number. For technical questions related to this position, contact restrepojm@ornl.gov.

About ORNL:

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for e