Job Description

Day to Day Responsibilities:
• Serve as a Scientific Machine Learning (SciML) Scientist, bridging physics‑based modeling and modern deep learning to address complex manufacturing and engineering challenges.
• Develop hybrid modeling frameworks that integrate mechanistic solvers with data‑driven ML components, enabling robust, interpretable, and scalable SciML workflows.
• Build high‑performance AI surrogate models to accelerate numerical simulations (e.g., CFD, FEA), reducing computational turnaround time while preserving physical consistency.
• Provide technical leadership for SciML research initiatives supporting Corning RD&E, guiding projects from problem formulation through model validation and deployment.
• Lead the development and customization of novel SciML solutions for manufacturing systems across multiple Corning businesses, translating customer and engineering needs into data‑driven solution designs and work plans.
• Collaborate with global, cross‑functional teams, effectively bridging domain knowledge (engineering and science) with computational and AI solution development.
• Design and execute model validation and testing strategies, and communicate technical analyses, insights, and results to internal customers and stakeholders.
• Contribute to the creation and protection of intellectual property, supporting patents and proprietary innovations in SciML.
• Actively participate in and help grow Corning’s AI community, sharing knowledge and promoting impactful applications of advanced analytics and SciML.

Required skills:
• Strong Python programming skills to perform scientific data preparation and ETL workflows, including cleaning, transforming, structuring, and curating multi fidelity simulation and experimental data for SciML and hybrid physics ML models.
• Hands‑on experience of designing, training, and evaluating SciML and physics‑informed ML models using industry‑standard Python libraries (e.g., SciPy, scikit‑learn, TensorFlow, Keras) and their integration with physical constraints, governing equations, and boundary conditions.
• Strong English written and verbal communication skills, enabling effective collaboration with global multifunctional teams and clear communication of technical results.
• Willingness to support global collaboration with 10-15% international travel as required.