Overview
This opportunity invites MSc, PhD candidates, and postdoctoral researchers in Materials Science or Chemistry to participate in a 1.5-month image annotation study. The project focuses on enhancing the quality of AI-generated annotations for scientific figures in the field of materials and surface chemistry. This initiative is significant as it contributes to improving data interpretability and quality in scientific research.
Background & Relevance
Materials Science and Chemistry are pivotal fields that drive innovation in various industries, including electronics, energy, and nanotechnology. The integration of AI in these domains has the potential to accelerate research and development processes. This study aims to refine AI-generated annotations, which are crucial for accurately interpreting complex scientific data. By engaging researchers in this annotation process, the study not only enhances AI capabilities but also fosters collaboration between AI and domain experts.
Key Details
- Compensation: €2,300 (fixed), paid upon full completion within 1.5 months
- Start Date: Immediate; rolling selection
- Number of Positions: Approximately 10 (subject to adjustment)
- Selection Process: Short, unpaid trial on one paper after receiving brief guidelines
Eligibility & Participation
This opportunity is strictly for candidates with a demonstrable background in Materials Science or Chemistry. It targets individuals who are currently pursuing or have completed advanced degrees in these fields, ensuring that participants possess the necessary expertise to contribute effectively to the study.
Submission or Application Guidelines
To apply, candidates should email both Fahad Ahmed and Jennifer D’Souza with the subject line “ALD/ALE Annotation Study – Application.” Applications must include:
1. A short CV
2. 3–4 lines detailing the applicant’s fit and background relevant to the study
Contacts:
Fahad Ahmed — fahad.ahmed [at] tib.eu
Jennifer D’Souza — jennifer.dsouza [at] tib.eu
Additional Context / Real-World Relevance
The role of AI in scientific research is becoming increasingly vital, particularly in data analysis and interpretation. By participating in this study, researchers will not only contribute to the advancement of AI technologies but also gain valuable experience in the intersection of AI and materials chemistry. This project exemplifies the collaborative efforts needed to enhance the quality of scientific research through innovative approaches.
Conclusion
This is an excellent opportunity for those in the field of Materials Science and Chemistry to engage in a meaningful project that combines their expertise with cutting-edge AI technology. Interested candidates are encouraged to apply and share this opportunity within their networks to help identify suitable participants for this important study.
Category: Internships & Student Roles
Tags: materials science, chemistry, surface chemistry, image annotation, ai-generated data, annotation study, ald, ale
