We are particularly interested in candidates who are comfortable solving complex technical problems where data may be incomplete, inconsistent, or scarce, and who can combine first-principles reasoning with empirical evidence and modern quantitative methods to develop defensible, scalable solutions. Model damage, recovery, and economic impacts by developing estimates of structural damage, repair costs and timelines, downtime, and indirect impacts using approaches including engineering first principles, cost estimation, statistical methods, and machine learning.