The Statistical Engineering Division at the National Institute of Standards and Technology is seeking immediate applications for a post-doctoral position focusing on leading 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:
- Leading the development of scientific computing applications and tools by partnering with domain experts, understanding the fundamental physics and meaning of the data, understanding stakeholder goals, and translating those goals into actionable software development plans and requirements.
- Guiding the creation and deployment of a data repository to store, maintain, and share results of interlaboratory measurements and analyses
- Guiding and 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
- Guiding and 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:
- Doctoral degree in at least one of the following fields: computer science, systems engineering, statistics, physics, or a closely-related discipline
- Proven leadership in cross-disciplinary environments; able to guide collaborative teams and provide strong scientific communication.
- 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.
PREP0005055
