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Postdoctoral Research Associate - Machine Learning for Analysis and Control of Complex System
Oak Ridge National Laboratory in Oak Ridge, Tennessee
 
 
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Date Posted 04/18/2021
Category
Admin-Tutors and Learning Resources
Employment Type
Application Deadline Open until filled
 
 
 
 
 

Requisition Id 5687 

Overview: 
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 in excess of 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 signal processing, statistical analysis, probabilistic theory and machine learning, particularly as applicable to prognostics or the control of complex engineered systems. 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 1.4 MW accelerator and target systems.
 
Major Duties and Responsibilities: 
•    Develop, implement and apply new statistical and machine-learning (ML) algorithms, including reinforcement learning algorithms, for sensor and component health monitoring, anomaly detection and fault isolation and prediction and system control based on data
•    Develop and apply both first-principles-based and data-driven techniques to solving complex engineering problems
•    Perform uncertainty quantification and uncertainty propagation analyses

 

Basic Qualifications:
•    PhD in nuclear, electrical engineering, mechanical, computer engineering, or engineering physics, computational science or a related field within the past 5 years.
•    Experience with open-source machine-learning frameworks, such as TensorFlow, Keras or pyTorch.
•    Strong understanding of underlying mathematics of signal processing, filtering, control theory and machine learning to unfold unique signatures in typically noisy time-series data
•    Demonstrated experience in statistical methods and machine-learning methods, with a specific application to time-series datasets from multiple sensors
•    Experience working in Linux environments on computer clusters.
•    Excellent communication skills (verbal, presentation and scientific writing) that enable effective interaction with technical peers, program managers, and sponsors
 
Preferred Qualifications:
•    Experience with applying and deploying recent machine-learning methods for solving complex engineering problems, including diagnostics and prognostics of complex engineered systems and system control
•    Experience in physics-informed machine learning and reinforcement learning for analysis of engineering systems
•    Experience with uncertainty quantification methods and application of those methods in complex systems
•    Demonstrated results-oriented problem-solving skills, and a willingness to apply those skills to a variety of engineering problems
•    Ability to work both independently and in a team environment, thoroughly document work performed
•    Strong scholarly and publication record that demonstrates independence and initiative taking

 
Additional Information:
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 up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

 

Benefits at ORNL:
UT-Battelle offers a quality benefits package, including a matching 401(k), contributory pension plan, paid vacation, and medical/dental plan options.  Onsite amenities include a credit union, medical clinic, cafeteria, coffee stands, and fitness facilities.
Relocation:
Moving can be overwhelming and expensive.  UT-Battelle offers a generous relocation package to ease the transition process.  Domestic and international relocation assistance is available for certain positions.  If invited to interview, be sure to ask your Recruiter (Talent Acquisition Partner) for details.
For more information about our benefits, working here, and living here, visit the “About” tab at 
https://jobs.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.


Nearest Major Market: Knoxville


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