Bayesian Research Jobs (NOW HIRING)

Requisition Id 17078 

Overview:

We are seeking a Postdoctoral Research Associate to join the Data Science and Engineering for Nonproliferation Group in the National Security Sciences Directorate (NSSD). In this role, you will conduct fundamental research into the integration of Bayesian methodologies with system dynamics modeling, advancing statistical methods and the open-source scientific software that allow time-evolving models of complex systems to be calibrated against sparse, indirect, and uncertain observations.

The group develops and maintains an open-source Python framework for building system dynamics models and for converting those models into probabilistic programs so their parameters can be inferred from data. The successful candidate will extend the mathematical and computational foundations of that capability and help bridge the gap between Bayes theory and practical application: knowledge integration, developing robust likelihood frameworks, sampler behavior for long autoregressive time series, diagnostics and model comparison, sensitivity and identifiability analysis, intuitive model interrogation methods and calibration when observations are few or conflicting. You will work alongside data scientists, software engineers, statisticians, and domain experts, publish and release your work openly, and apply these methods to nuclear nonproliferation and nuclear fuel cycle problems, where consequential judgments must be made from incomplete evidence.

 Major Duties/Responsibilities:

• Collaborate with researchers and mentors to develop and apply Bayesian methods for calibrating dynamic system models and quantifying uncertainty in their predictions.
• Conduct fundamental research on the formulation of probabilistic system dynamics models, including knowledge integration, likelihood frameworks, sampler configuration and performance, convergence diagnostics, and posterior predictive checking.
• Contribute to the design, implementation, testing, and documentation of open-source scientific software that makes these methods usable and reproducible for other researchers.
• Design and execute computational studies that assess model performance, sensitivity, and parameter identifiability, and that communicate uncertainty in a form useful to analysts and decision makers.
• Deliver R&D on an ongoing basis as evidenced by publications, S&T presentations, professional community engagement, software releases, and inventions or copyrights as appropriate.
• Exercise scientific integrity in performing and communicating research.
• Ensure all work is carried out safely, securely, and in compliance with ORNL policies, standards, and procedures.
• Ability to engage in domestic and international travel as required.

Basic Qualifications:

• Ph.D. in statistics, applied mathematics, computer science, physics, engineering, operations research, or a related quantitative discipline.
• Demonstrated experience applying Bayesian methods to scientific or engineering problems, including prior specification, likelihood formulation, posterior sampling, and assessment of convergence and model fit.
• Proficiency in Python and the scientific computing stack, with experience developing software for scientific, statistical, or numerical computing.
• Strong written and verbal communication skills, including experience presenting scientific results to technical communities and at professional society conferences and workshops.

 Preferred Qualifications:

• Experience with probabilistic programming frameworks such as PyMC, Stan, NumPyro, or similar.
• Experience with system dynamics or compartmental modeling — stock-and-flow formulations, feedback structure, and simulation of coupled difference or differential equations.
• Experience calibrating simulation models against sparse, indirect, aggregated, or otherwise limited observations.
• Familiarity with the computational foundations of probabilistic programming, such as automatic differentiation, tensor libraries (PyTensor, JAX), gradient-based samplers, or model transpilation and compilation.
• Experience with uncertainty quantification, global sensitivity analysis, parameter identifiability, surrogate modeling, or Bayesian model selection and comparison.
• Experience contributing to open-source scientific software, including version control, testing, continuous integration, documentation, and code review.
• Knowledge of nuclear nonproliferation, international safeguards, arms control, nuclear fuel cycle analysis, or related national security mission areas.
• Experience translating model output into decision-relevant products for analysts, sponsors, or policy audiences, including interactive visualization or exploratory user interfaces.
• Record of peer-reviewed publications, conference presentations, or other research accomplishments.
• Experience working in multidisciplinary research environments involving both domain scientists and software developers.
• Experience working with DOE National Laboratories or similar R&D organizations.

Special Requirements: 

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.

• Visa sponsorship: Visa sponsorship is not available for this position.

• Security, Credentialing, and Eligibility Requirements: Q Clearance with SCI: This position requires the ability to obtain and maintain a Secret Compartmented Information (SCI) clearance from the Department of Energy. As such, this position is a Workplace Substance Abuse (WSAP) testing designated position. WSAP positions require passing a pre-placement drug test and participation in an ongoing random drug testing program. In addition, due the SCI, you may also be subject to random polygraph testing.

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 employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation. 

ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience. 

Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, 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. 

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.