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Performance Evidence Definition: You translate system-level and scale-up questions into clear experimental objectives and KPIs, define required datasets and operating windows, and ensure that tests deliver reliable, traceable and decision-ready performance evidence.
Experimental Data Analysis: You process and structure KPI-based datasets with statistical rigor, identify trends and operating limits, evaluate thermal or transport constraints and quantify uncertainties to gain robust insights from experimental results.
Model Validation & Risk Identification: You compare model predictions with experimental results, spot calibration gaps and model assumptions needing refinement, diagnose deviations and propose targeted improvements while contributing to standardized validation workflows.
Cross-functional Technical Alignment: You collaborate closely with Process Development and Technology to align KPIs, test plans and interpretations, ensure clear technical communication and maintain structured documentation.
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