Uncertainty Quantification for Surrogate Models Postdoctoral Researcher

About the role

We have an immediate opening for a Postdoctoral Researcher to perform research and development as well as verification and validation of uncertainty quantification (UQ) methods for surrogate models. Deep Gaussian processes as well as scalable Gaussian processes are of particular interest. You will work independently as a technical expert and will interact with other researchers in statistics, UQ, applied mathematics, and machine learning/AI. This position is in the Center for Applied Scientific Computing (CASC) Division within the Computing Principal Directorate.

Responsibilities

• Conduct basic research in efficient Gaussian processes to understand conditions under which their resulting uncertainties agree with other UQ metrics for AI surrogate models.
• Collaborate with others in a multidisciplinary team environment to accomplish research goals including industrial and academic partners.
• Develop, implement, validate, and document specialized analysis software tools and models as required.
• Organize, analyze and publish research results in peer-reviewed scientific or technical journals and present results at external conferences seminars and/or technical meetings.
• Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
• Perform other duties as assigned.

Requirements

• Ph.D. in Statistics, Applied Mathematics, or a related field.
• Experience with deep Gaussian processes.
• Knowledge of ongoing work in scalable Gaussian processes.
• Experience with functional data.
• Knowledge of AI surrogates (e.g., neural networks) and associated UQ methods.
• Experience using programming skills in at least one prototyping language R/Matlab/Python.
• Knowledge of an ML library (TensorFlow, PyTorch, or JAX).
• Experience developing independent research projects as demonstrated through publication of peer-reviewed literature.
• Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
• Effective initiative and interpersonal skills and ability to work in a collaborative, multidisciplinary team environment.

Qualifications

• Ph.D. in Statistics, Applied Mathematics, or a related field.
• Experience with deep Gaussian processes.
• Knowledge of ongoing work in scalable Gaussian processes.
• Experience with functional data.
• Knowledge of AI surrogates (e.g., neural networks) and associated UQ methods.
• Experience using programming skills in at least one prototyping language R/Matlab/Python.
• Knowledge of an ML library (TensorFlow, PyTorch, or JAX).
• Experience developing independent research projects as demonstrated through publication of peer-reviewed literature.
• Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
• Effective initiative and interpersonal skills and ability to work in a collaborative, multidisciplinary team environment.

Desired Qualifications

• Familiarity with active learning/sequential design
• Experience with splines and associated UQ methods
• Experience with high-performance computing systems (i.e., parallel programming libraries such as MPI)

Benefits

• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)

Pay

$138,480 Annually

Schedule

Full-time