Post Doctoral Research Fellow

Post Doctoral Research Fellow

Join to apply for the Post Doctoral Research Fellow role at Boise State University School of Nursing

Boise,

Non-Classified/Professional

The Postdoctoral Research Fellow will specialize in computational modeling and data science for water and energy resilience and join a transdisciplinary team as part of the NSF‑funded Idaho Community‑engaged Resilience for Energy‑Water Systems (I‑CREWS) project. This initiative brings together faculty, students, and partners across Idaho to advance innovative solutions to the urgent challenges of energy‑water (E‑W) system resilience in the face of technological, population, and environmental change.

The Postdoctoral Research Fellow will be based at Boise State University and work closely with I‑CREWS researchers across the state, emphasizing the development, integration, and application of modeling approaches—including machine learning (ML), hydrologic and energy systems simulations, and scenario forecasting—to evaluate dynamic energy‑water futures and resilience strategies for diverse Idaho communities.

• Problem solve research challenges and model development, as applicable.
• Contribute discrete components of a larger project under the general direction of a senior or principal researcher.
• Prepare complete documentation of research activities for reference purposes, including following applicable data management protocols.
• Analyze, interpret, and document research findings for review.
• Compare actual and expected results and suggest solutions to correct deviations.
• Evaluate existing research or modeling techniques and recommend improvements to ensure quality and productivity.
• Assist in the preparation of proposals and reports.
• Assist in teaching classes.
• Develop and apply computational models (ML, hydrologic, and energy systems) to assess the resilience of coupled energy‑water systems under technological, population, and environmental stressors.
• Conduct scenario analyses and alternative futures forecasting for Idaho study site communities within the I‑CREWS resilience framework.
• Integrate/interweave local and Indigenous knowledge and governance structures, as appropriate, with quantitative models to identify locally relevant resilience strategies.
• Collaborate with interdisciplinary teams of social scientists, engineers, natural scientists, and Tribal partners.
• Manage and analyze large heterogeneous datasets relevant to energy‑water resilience.
• Prepare manuscripts for publication in peer‑reviewed journals and present research results at conferences.
• Mentor and collaborate with graduate and undergraduate researchers at Boise State.
• Perform other duties as assigned.

• Must meet eligibility requirements for work in the United States at the time the appointment is scheduled to begin and to continue working legally for the proposed term of the appointment.
• Strong written and verbal communication skills.
• Excellent teamwork and communication skills.
• Scientific writing skills demonstrated through peer‑reviewed publications.
• Ability to acquire accurate, reproducible data.
• Critical eye towards consistency, reproducibility, and the scientific method in general.
• Excellent project and time management skills.
• Strong interpersonal and mentoring skills.
• Advocate for safety and ethics in the workplace.

Requires a minimum of a Ph.D. or (ABD) in engineering, hydrology, computational sciences, systems modeling, applied mathematics, or a related field.

• Demonstrated expertise in quantitative modeling and data analysis.
• Proficiency in at least one of the following: hydrologic modeling, energy systems modeling, systems simulation, or machine learning/AI.
• Ability to work effectively on interdisciplinary, team‑based research projects.
• Experience integrating/interweaving qualitative inputs (e.g., policy, etc.) with quantitative models and/or machine learning.
• Familiarity with MODFLOW, MATPOWER, OpenDSS, Machine Learning based emulators, or agent‑based modeling.
• Knowledge of scenario development, resilience frameworks, and socio‑environmental‑technological…