Postdoc: Decision Science for Community Resilience

1000scholars; Baltimore (MD); On-site; USD 65,000 - 90,000; Full time

Job summary

Johns Hopkins University seeks a PREP Research Associate to support the NIST PREP program focused on Decision Science and Computational Methods for Community Resilience. You will help standardize and analyze heterogeneous field data, create data schemas, and develop reproducible workflows for multimodal sensing and time-series integration.

The role requires programming proficiency, experience with ML/NLP methods, and strong communication to collaborate with NIST researchers and external

Qualifications

• PhD or 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.

Responsibilities

• 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.
• Develop and evaluate standardized schemas, metadata elements, controlled vocabularies, data dictionaries, provenance records, and quality-control rules for multimodal community resilience field data.
• Design reproducible workflows to ingest, clean, validate, synchronize, link, transform, and version heterogeneous data sources, including time-series sensing and location data.
• Support design and analysis of field-data collection activities, including mobile sensing and infrastructure monitoring using signals such as acceleration, location, sound, and related environmental data.
• Develop and test statistical, machine-learning, and NLP methods to classify observations and extract entities, relationships, and causal structures from unstructured records, linking outputs to structured datasets.
• Develop research software or analytical prototypes, communicate findings in internal and stakeholder meetings and technical publications, and ensure datasets, code, and documentation are reproducible and archived.

Education

PhD or ABD

Tools

Version control

Job description

Johns Hopkins University seeks a PREP Research Associate to support the NIST PREP program focused on Decision Science and Computational Methods for Community Resilience. You will help standardize and analyze heterogeneous field data, create data schemas, and develop reproducible workflows for multimodal sensing and time-series integration.

The role requires programming proficiency, experience with ML/NLP methods, and strong communication to collaborate with NIST researchers and external