Postdoctoral Research Associate - Machine Learning for Complex System Prognostics and Diagnostics

Key Skills

Analytical and Problem solving, Effective communication skills, Keras software library, Linux Operating system, Machine learning techniques, MLflow, PyTorch, Statistical modeling, TensorFlow

Job Description

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an extraordinary 80-year history of solving the nation’s biggest problems. We have a dedicated and creative staff of over 6,000 people! Our vision for diversity, equity, inclusion, and accessibility (DEIA) is to cultivate an environment and practices that foster diversity in ideas and in the people across the organization, as well as to ensure ORNL is recognized as a workplace of choice. These elements are critical for enabling the execution of ORNL’s broader mission to accelerate scientific discoveries and their translation into energy, environment, and security solutions for the nation.

The Neutron Sciences Directorate (NScD) at Oak Ridge National Laboratory (ORNL) operates the High Flux Isotope Reactor (HFIR), the United States' highest flux reactor-based neutron source, and the Spallation Neutron Source (SNS), the world's most intense pulsed accelerator-based neutron source. Together these facilities operate 30 instruments for neutron scattering research, each year carrying out more than 1,000 experiments in the physical, chemical, materials, biological and medical sciences. HFIR also provides unique facilities for isotope production and neutron irradiation. To learn more about Neutron Sciences at ORNL go to: http://neutrons.ornl.gov. Oak Ridge National Laboratory is also a leader in computational and computer science, with unique strengths in high-performance computing and data analytics with applications to the physical and biological sciences.

We are seeking a postdoctoral research associate who will focus on machine learning, signal processing, and statistical analysis with emphasis on prognostics and applications. This position resides in the Accelerator Science and Technology Section in the Research Accelerator Division, Neutron Sciences Directorate at Oak Ridge National Laboratory (ORNL). 

As part of our research team, you will work with accelerator and target systems specialists and machine learning experts to develop, integrate, and apply machine learning methods to improve performance of the SNS 1.7 MW accelerator and target systems.

Major Duties/Responsibilities:

• Develop, implement, and apply novel machine-learning (ML) and statistical methods to sensor and component health monitoring with reporting, anomaly detection, and fault isolation of complex dynamic systems.
• Develop and apply both first principles-based and data-driven techniques to solving complex engineering problems.
• Perform data analysis on large, sparse, and noisy data.
• Perform uncertainty quantification and uncertainty propagation analyses.
• Test, validate, monitor, and maintain deployed ML models on field to ensure operational success.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote diversity, equity, inclusion, and accessibility by fostering a respectful workplace – in how we treat one another, work together, and measure success.

Basic Qualifications:

• A PhD in nuclear, electrical engineering, mechanical, computer engineering, engineering physics, computational science, or a related field completed within the last 5 years

Preferred Qualifications:

• Basic understanding of ML methodologies and their applications to complex engineering systems.
• Experience with open-source machine-learning tools, such as TensorFlow, Keras, PyTorch, and MLflow.
• Experience with applying and deploying state-of-the-art machine-learning methods for solving complex engineering problems, including diagnostics and prognostics of complex engineered systems.
• Experience with sustainable machine-learning ecosystems, maintaining deployed machine-learning models on field, and adapting to evolving system conditions via continual learning approaches.
• Experience in physics-informed machine learning for analysis of physical systems.
• Experience with uncertainty quantification methods and application of those methods in complex systems.
• Experience working in Linux environments on large high-performance cluster and GPU computing architectures.
• Demonstrated experience in statistical methods and machine-learning methods, with a specific application to time-series datasets from multiple sensors.
• Strong understanding of underlying mathematics of signal processing, filtering, and machine learning to unfold unique signatures in typical noisy time-series data.
• Demonstrated results-oriented problem-solving skills, and willingness to apply those skills to a variety of engineering problems.
• Excellent communication skills (verbal, presentation, and scientific writing) that enable effective interaction with technical peers, program managers, and sponsors.
• Strong scholarly and publication record that demonstrates independence and initiative taking.
• Ability to work independently and in a team environment, thoroughly document work performed.
• Excellent written and oral communication skills
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be for up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and the availability of funding.

Please submit three letters of reference when applying to this position. You can upload these directly to your application or have them sent to postdocrecruitment@ornl.gov with the position title and number referenced in the subject line.

Instructions to upload documents to your candidate profile:

• Login to your account via jobs.ornl.gov
• View Profile
• Under the My Documents section, select Add a Document

Benefits at ORNL: 

ORNL offers competitive pay and benefits programs to attract and retain talented people. The laboratory offers many employee benefits, including medical and retirement plans and flexible work hours, to help you and your family live happy and healthy. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also provided for convenience.

Other benefits include: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Life Insurance, Pet Insurance, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.

If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov.

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.

If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.