The Statistical Engineering Division at the National Institute of Standards and Technology is seeking immediate applications for a post-bachelor's position focusing on supporting the development of scientific computing applications and tools. The tools will include both public-facing web infrastructure and code bases used by bench scientists for data collection and analysis. A primary focus will be establishing a central, high-resolution data repository for international clock and frequency standard measurements, providing essential data and collaborative infrastructure for the upcoming redefinition of the second.
The candidate's primary responsibilities may include:
- Supporting domain experts who generate requirements documents outlining software development needs
- Creation and deployment of a data repository to store, maintain, and share results of interlaboratory measurements and analyses.
- Performing the data curation and data engineering work to transform raw data into machine actionable datasets as guided by the FAIR data principles
- Managing and consolidating data streams originating from atomic clocks and the associated network components including, e.g., optical fiber links and optical frequency combs.
- Developing methodology for collating complex datasets involving multimodal data or heterogeneous processing pathways
- Extracting information from the curated data using statistical techniques and/or AI approaches
The ideal candidate will have the following skills, experience, and/or qualifications:
- Bachelor's degree in at least one of the following fields: computer science, systems engineering, statistics, physics, or a closely-related discipline
- Cross-disciplinary collaboration.
- Experience in scientific computing, workflow development and data management.
- Applied machine learning / data science.
- Reproducible research and production/dissemination of archival datasets.
- Enthusiasm for learning about diverse measurement science techniques.
- Familiar with DevOps principles and container-based application development/deployment.
Keywords: Python, DevOps, Docker, Kubernetes, Linux, Bash, Amazon Web Services, JSON, XML, HTML, JavaScript, CSS
