Postdoctoral positions at Sandia National Laboratories

The International Society for Bayesian Analysis; Albuquerque (NM); On-site; USD 60,000 - 100,000; Full time

Job summary

An innovative research opportunity awaits at a leading national laboratory, where you will join a dynamic team of experts tackling significant national security challenges. As a Postdoctoral candidate, you will engage in groundbreaking projects that blend statistical science with machine learning, focusing on climate impacts and anomaly detection in real-time data. This role is perfect for those passionate about applying advanced statistical methodologies to pressing issues, offering a chance to contribute to impactful research in a collaborative environment. If you are eager to push the boundaries of statistical science and make a difference, this position is for you.

Qualifications

• PhD in Statistics or a related field is required.
• Experience with machine learning and statistical modeling techniques.

Responsibilities

• Develop statistical methods for climate impact analysis.
• Collaborate with interdisciplinary teams on data-driven projects.

Skills

Statistical Analysis

Machine Learning

Bayesian Methods

Data Analysis

Statistical Modeling

Education

PhD in Statistics or related field

Tools

Python

R

MATLAB

Job description

Postdoctoral positions at Sandia National Laboratories

The Statistical Sciences Department at Sandia National Laboratories seeking motivated Postdoctoral candidates for two exciting projects. As a department, we provide cutting edge statistical expertise to help solve the most challenging national security problems and lead the advancement of statistical methodologies for applications in the national interest.

The first project is focused on developing statistical and interpretable machine learning methods to discover meaningful connections between subsequent climate impacts arising from a significant climate event. In this role, you will work with an interdisciplinary team of climate scientists, statisticians, computer scientists, and more to develop cutting-edge spatio-temporal statistical methods that are both computationally efficient and able to capture the complex dynamics in the climate.

The second focuses on developing unsupervised statistical learning techniques for detecting, predicting and quantifying anomalies in large multivariate real-time data. This will leverage key ideas from state-space dynamic modeling, variational inference methods and Bayesian nonparametrics. This role will involve collaborating with computer scientists and subject matter experts across a range of distinct mission-driven areas.