Expert Mentor – AI-Driven Polymer Composites & Materials Informatics

NSTC | NanoSchool is seeking a highly qualified and experienced Freelance Workshop Mentor to conduct an advanced, hands-on research workshop titled: AI-Driven Polymer Composites: Microstructure Analysis, Property Prediction & Inverse Design We invite applications from PhD-qualified researchers, postdoctoral fellows, professors, scientists, and industry experts with strong expertise in polymer composites, materials informatics, artificial intelligence, and machine learning. The selected mentor will be responsible for delivering research-oriented online sessions, demonstrating computational workflows, and providing practical guidance to an international audience of researchers, academicians, PhD scholars, and industry professionals. Key Responsibilities Conduct advanced, interactive online workshop sessions with hands-on demonstrations. Deliver practical training in AI/ML applications for polymer composite research. Demonstrate microstructure analysis and quantitative characterization using microscopy images. Guide participants through machine learning-based mechanical and thermal property prediction. Explain structure–property relationships and data-driven materials optimization. Demonstrate inverse design approaches for identifying suitable polymer composite formulations. Prepare presentations, research datasets, Python notebooks, and hands-on exercises. Provide scientific explanations, technical support, and answers to participant queries. Coordinate with the NSTC program team for workshop planning, content review, and successful session delivery. Required Technical Expertise Candidates should demonstrate strong expertise in the following areas: Polymer composites, nanocomposites, and advanced materials characterization. SEM image analysis, microstructure quantification, and image processing. Materials informatics and computational materials science. Artificial intelligence and machine learning for materials research. Mechanical, thermal, and physicochemical property prediction. Python programming, Scikit-learn, Pandas, and Google Colab. ImageJ/Fiji or similar microscopy image analysis tools. Structure–property relationships, feature engineering, and predictive modeling. Inverse materials design and formulation optimization. Eligibility Criteria Mandatory Qualifications Completed PhD in Materials Science, Polymer Science, Polymer Engineering, Chemical Engineering, Nanotechnology, or a closely related discipline. Demonstrated research expertise in polymer composites or related advanced materials. Practical knowledge of AI/ML-based materials characterization or property prediction. Ability to independently conduct hands-on computational demonstrations. Strong technical communication and presentation skills in English. Preferred Qualifications Postdoctoral, academic, or industrial research experience. Publications in reputed peer-reviewed scientific journals. Experience conducting international workshops, research training programs, or technical webinars. Expertise in Python-based materials informatics and machine learning workflows. Workshop Engagement Details Workshop Format: Live, mentor-led online training. Duration: 3 days, approximately 60–90 minutes per session. Working Arrangement: Freelance, project-based engagement. Schedule: As per workshop requirements and mutually agreed availability. Compensation: Professional remuneration per workshop/session, finalized before engagement. Please Note: This is a freelance mentorship opportunity, not a full-time or permanent employment position. Application Process Interested candidates should submit: Updated CV or academic profile. PhD qualification and current professional designation. Relevant research experience and selected publications. Expertise in polymer composites, AI/ML, and computational tools. Previous workshop or training experience, if applicable. Availability and expected remuneration. Workshop Details: https://nanoschool.in/aiws/ai-driven-polymer-c…

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