Postdoctoral Fellow (PREP0005175)

PREP Research Associate

This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest, thus requires that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.

Research Title:

Decision Science and Computational Methods for Community Resilience

The work will entail:

The associate will support NIST Community Resilience research by developing methods to standardize, integrate, and analyze heterogeneous field data used to characterize infrastructure, community conditions, and resilience outcomes. The work will focus on creating consistent data structures, metadata, data dictionaries, quality-control procedures, and reproducible computational workflows that allow data collected across field studies, communities, and time periods to be compared and combined. Data may include mobile or fixed sensor measurements, time-series data, accelerometer and gyroscope measurements, GPS or other geospatial information, infrastructure observations, surveys, interviews, and other qualitative records. The associate will also investigate machine-learning and natural-language-processing methods for extracting entities, relationships, and causal information from field and interview data and linking those representations with quantitative measurements. The research will contribute to field-data collection strategies, interoperable data products, analytical prototypes, technical documentation, reports, and presentations for NIST researchers and external collaborators.

Key responsibilities will include but are not limited to:

• Develop and evaluate standardized schemas, metadata elements, controlled vocabularies, data dictionaries, provenance records, and quality-control rules for multimodal community resilience field data, including sensor, geospatial, infrastructure, survey, and interview data.
• Design reproducible workflows to ingest, clean, validate, synchronize, link, transform, and version heterogeneous data sources, with particular attention to integrating time-series sensing and location data with contextual or qualitative information.
• Support the design and analysis of field-data collection activities, including mobile or smartphone-based sensing and infrastructure monitoring using measurements such as acceleration, angular motion, location, sound, or related environmental and operational signals.

* Develop and test statistical, machine-learning, and natural-language-processing methods to classify observations and extract entities, relationships, and causal structures from unstructured records such as interviews or transcripts, and connect these outputs to structured field datasets.

* Develop research software or analytical prototypes, communicate findings in internal and stakeholder meetings and technical publications, and ensure that datasets, code, protocols, model outputs, and documentation are reproducible and archived for use by the larger NIST research program.

* A PhD degree, or current PhD candidacy with all degree requirements completed except the dissertation (all-but-dissertation/ABD), in Computational Science, Data Science, Computer Science, Engineering, Applied Mathematics, Systems and Control, or a closely related field.

* Three or more years of relevant research or professional experience applying computational methods to heterogeneous scientific, engineering, infrastructure, transportation, or field-collected data.

* Demonstrated experience with multimodal sensing or time-series data, preferably including accelerometer, gyroscope, GPS/geospatial, acoustic, smartphone, or related field measurements used for infrastructure or transportation monitoring.

* Experience developing machine-learning or statistical methods for classification, inference, data fusion, or pattern discovery across heterogeneous data sources, together with the ability to prototype reproducible data-processing and analysis workflows.

* Experience with natural-language processing or language-model-based methods for entity and relationship classification, information extraction, causal-model extraction, or analysis of interviews, transcripts, or other unstructured text is highly desirable.

* Proficiency with programming and scripting tools used in computational research, along with familiarity with version control, reproducible research practices, structured documentation, and collaborative software or data workflows.

* Strong oral and written communication skills demonstrated through interdisciplinary research collaboration, technical presentations, publications, workshops, or mentoring, and the ability to work effectively with NIST researchers and external stakeholders.

U.S. Citizens are Preferred

Please upload the following with your application:

- CV/Resume

*Please limit C.V to 3 pages only and ONLY include a valid email address for your contact info. Your resume will not be considered if the following information is included on your CV/resume.

- Self portraits

- Phone number

- Home address/Country

- Citizenship status

- Languages spoken

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Authority: 15 U.S.C. * 278g-1(e)(1) and (e)(3) and 15 U.S.C. * 272(b) and (c)

Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP)which isdesigned to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.

PREPis a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate the administrative functions of the PREP Program.

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Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated. By applying to a CHIPS-funded PREP opportunity, you also acknowledge that participation in the project requires signing a Non-Disclosure Agreement (NDA) prior to beginning any work.

Salary Range

The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.

Total Rewards

Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits-worklife/.

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The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, the university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristics. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors or demographic characteristics that are irrelevant to the program involved.

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Background Checks

After receiving a conditional offer, the successful candidate(s) for this position will be subject to a pre-employment background check including education verification. When deciding whether a candidate's conviction history is job-disqualifying, the University considers the nature and gravity of the offense, the time that has passed since the conviction, and the nature of the job being sought.

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Johns Hopkins University strongly encourages, but no longer requires, at least one dose of the COVID-19 vaccine. This change does not apply to the School of Medicine (SOM). SOM hires must be fully vaccinated with an FDA COVID-19 vaccination and provide proof of vaccination status. We still require all faculty, staff, and students to receive the seasonal flu vaccine.

Exceptions to the seasonal flu vaccine or COVID-19 vaccine (for SOM) requirement(s) may be provided to individuals with sincerely held religious beliefs or medical conditions that preclude them from receiving the vaccine. Requests for an exception must be submitted to the JHU vaccination registry. For additional information, applicants for SOM positions should visit https://www.hopkinsmedicine.org/coronavirus/covid-19-vaccine/ and all other JHU applicants should visit https://covidinfo.jhu.edu/health-safety/covid-vaccination-information/.

The following additional vaccine requirements may apply, depending upon your campus. Please contact the hiring department for more information.

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