PhD Position in Corrosion Detection: Join the innovative team at Delft University of Technology and contribute to military corrosion technology through data science. This PhD position focuses on utilizing Artificial Intelligence and Machine Learning for detecting and predicting localized corrosion in military systems, aiming for groundbreaking advancements in material durability and safety.
PhD Position in Corrosion Detection and Lifetime Prediction with AI
Designation:
PhD Researcher
Research Area:
Corrosion Technology, Electrochemistry, Data Science, Artificial Intelligence, Machine Learning
Location:
Delft University of Technology, Delft, Netherlands
Eligibility/Qualification:
- Master’s degree in Materials Science, Metallurgy, Mechanical Engineering, Chemical Engineering, or a related field.
- Proficiency in Data Science techniques.
- Strong analytical skills, creativity, initiative, and the ability to work collaboratively.
- Excellent communication skills in English.
- Candidates must be of Dutch and/or EU/NATO nationality.
Job Description:
As a PhD researcher, your responsibilities will include:
- Determining the required discrimination ability for classifying and quantifying corrosion phenomena.
- Investigating suitable AI/ML models for corrosion detection and quantification.
- Further developing AI/ML models based on electrochemical sensor data.
- Establishing training data requirements for the AI/ML models.
- Performing laboratory experiments to validate corrosion classification and quantification.
- Conducting further validation using field data from military systems.
How to Apply:
Interested candidates should submit their applications no later than January 4, 2026. The application must include:
- A detailed CV
- A motivation letter
- Two references
- The abstract of the MSc thesis in English
Applications should be addressed to Dr. Axel Homborg and/or Prof. Dr. Arjan Mol, and submitted via the designated application button on the official website. Applications sent via email will not be considered.
Last Date to Apply:
January 4, 2026
This post outlines a unique opportunity for aspiring researchers to work on critical issues in materials science while leveraging cutting-edge AI technologies.
