PhD proposal announcement
“Closed-Loop AI Platform for Mechanistic Investigation of Stainless-Steel Corrosion in Rainwaters”
Key words: corrosion, machine learning, automation, optical and synchrotron characterization
This PhD project will develop on an AI-driven, closed-loop corrosion platform to predict stainless steel corrosion in simulated rainwater, addressing the complexity of aqueous environmental conditions (>30 million possibilities). First, reinforcement learning will be implemented to identify corrosion extremes for stainless steels under various combinations of rainwater ions (sulfates, carbonates, ammonium, magnesium, calcium, etc.). Second, operando nanoscale passive layer thickness mapping (via optical microscopy) and synchrotron-based post-mortem chemical characterization will elucidate corrosion mechanisms under these optimal conditions. Finally, machine learning models will correlate passive layer characteristics, chemical composition, and solution chemistry to predict corrosion behavior.
Who Should Consider Joining This Journey?
We are looking for a passionate, driven, and innovative PhD candidate who:
• Possesses a foundational knowledge of AI & Machine Learning & Python or is eager to learn and expand their expertise.
• Holds a solid foundation in Chemistry or Materials Science: Prior exposure to electrochemistry, corrosion science, or materials characterization techniques will be beneficial.
• Thrives in Interdisciplinary Environments: A creative problem-solver with the ability to work collaboratively across diverse fields and adapt to challenging research landscapes.
If you’re eager to contribute to a project that combines cutting-edge AI with breakthrough experimental chemistry, let’s connect!
This is a unique opportunity to be at the forefront of corrosion research and to help
drive innovations that could redefine how we protect critical infrastructure.
Contact Details:
Dr. Slava (Viacheslav) SHKIRSKIY
ITODYS - CNRS UMR 7086
Université Paris Cité
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