Post Doctoral Researcher, Department of Geography and Spatial Sciences

University of Delaware; Newark (DE); Vor Ort; USD 65.000 - 90.000; Vollzeit

Benefits dieser Stelle

Conference travel support

Professional development

Zusammenfassung

The University of Delaware invites a Postdoctoral Researcher in Geography and Spatial Sciences to advance SCS risk assessments for insurance applications. You will work on present-day and near-term projections using large ensemble systems, collaborating with Columbia University, NOAA GFDL, and industry partners to develop decision-support tools.

Ideal candidates will have a Ph.D. by January 2027, experience with SPEAR/WRF/CESM, and strong Python/data-analysis skills.

Qualifikationen

• Ph.D. in a relevant field or must complete by January 2027.
• Experience with weather/climate model output (e.g., SPEAR, WRF, CESM).
• Proficiency in Python and handling large datasets.
• Strong written and verbal communication; able to work independently and in a team.

Aufgaben

• Develop data-driven models to simulate severe weather hazards in large ensembles.
• Evaluate model biases and uncertainties in near-term projections.
• Publish findings in peer-reviewed journals and present at meetings.
• Collaborate with research and industry teams on deliverables.

Kenntnisse

Python

Data analysis

Handling large datasets

Communication skills

Independent & collaborative work

Ausbildung

Ph.D. in Atmospheric Science / Meteorology / Climate Science / Geography

Tools

SPEAR

WRF

CESM

Jobbeschreibung

Post Doctoral Researcher, Department of Geography and Spatial Sciences

Job no: 503953
College / VP Area: College of Earth & Ocean Envir
Work type: Staff
Location: Newark, DE
Categories: Research & Laboratory

Pay Grade: 90E

Project Summary

The Department of Geography and Spatial Sciences at the University of Delaware is seeking a Postdoctoral Researcher to characterize current and future U.S. severe convective storm (SCS) risk for insurance applications. The project goals are to (1) estimate present-day and future SCS risk using simulations from large ensemble systems and (2) identify and quantify the uncertainty in historical trends and near-term model projections. The work will be in close collaboration with scientists at Columbia University and NOAA Geophysical Fluid Dynamics Lab as well as industry professionals across the (re)insurance sector to develop SCS risk assessments and decision-making tools/frameworks using near-term SCS projections.

Focus Area: Severe Weather, Climate Risk

Background

Severe convective storms (SCS) — thunderstorms that produce damaging winds, large hail, or tornadoes — are increasingly becoming a dominant driver of insured losses. Consequently, assessments of present-day as well as projected future SCS risk are of considerable interest and value to the insurance industry. Our group will use historical and future simulations in large ensembles to better resolve risk from SCS hazards like tornadoes and hail, and to tailor risk assessments and near-term SCS projections to meet the needs of the insurance industry.

Responsibility

• Develop data-driven models to simulate severe weather hazards like tornadoes and hail, and apply models to investigate severe weather hazard activity in large ensemble simulations.
• Evaluate model biases, e.g. from GFDL’s Seamless System for Prediction and EArth System Research (SPEAR) large ensembles.
• Identify and explore sources of uncertainty in near-term SCS projections, e.g., from interannual to decadal climate variability.
• Publish findings on peer-reviewed journals, present progress at professional meetings, prepare project reports and deliverables, and collaborate with other research and industry teams on a regular basis.

Qualifications

• Ph.D. in a relevant field (e.g., Atmospheric Science, Meteorology, Climate Science, Geography, or related areas) or should be completed with their Ph.D. degree requirements by January 2027.
• Experience with weather and/or climate model output (e.g., SPEAR, WRF, CESM), and familiarity with climate model frameworks (e.g., large ensembles).
• Proficiency in programming and data analysis (e.g., Python) and handling large datasets, such as from hindcasts, large ensembles, or Coupled Model Intercomparison Project.
• Strong written and verbal communication skills.
• Ability to work independently as well as effectively in a collaborative, multidisciplinary team environment.

Preferred Skills

• Experience with data-driven (including machine learning), physics-informed modeling approaches.
• Background in climate risk, i.e., applying weather and climate to societal impacts, decision-making, adaptation, etc.
• Familiarity with high-performance computing.

This position is in-person with support for conference travel and other professional development activities.

Notice of Non-Discrimination and Equal Opportunity

The University of Delaware does not discriminate against any person on the basis of race, color, national origin, sex, gender identity or expression, sexual orientation, genetic information, marital status, disability, religion, age, veteran status or any other characteristic protected by applicable law in its employment, educational programs and activities, admissions policies, and scholarship and loan programs as required by Title IX of the Educational Amendments of 1972, the Americans with Disabilities Act of 1990, Section 504 of the Rehabilitation Act of 1973, Title VI and VII of the Civil Rights Act of 1964, and other applicable statutes and University policies. The University of Delaware also prohibits unlawful harassment including sexual harassment and sexual violence.