The digital era has ushered in an unprecedented volume of data, with presentation slides as a primary medium for knowledge sharing in academic and professional settings. However, the efficient organization, retrieval, and understanding of these slides remain a challenge. This thesis aims to explore the integration of Machine Learning (ML) techniques, particularly in classifying presentation slides, to revolutionize knowledge management practices. By harnessing the capabilities of ML algorithms, we can significantly improve the accessibility and utility of presentation slides, thereby enhancing learning outcomes and knowledge dissemination.

This thesis seeks to develop a comprehensive ML model for the classification of presentation slides. Key objectives could include:

• Development of a Classification Model: Designing and training an ML model to accurately classify presentation slides based on content, context, and structure.
• Evaluation of ML Algorithms: Comparing the effectiveness of various ML algorithms in classifying presentation slides to identify the most efficient approach.
• Integration with Knowledge Management Systems: Exploring the potential for integrating the classification model with existing knowledge management systems to enhance information retrieval and dissemination.

The thesis will involve a combination of qualitative and quantitative research, including data analysis, and potentially user studies to evaluate the system's effectiveness and user satisfaction. There will be a particular focus on the practical application of the research, aiming to develop a prototype or proof-of-concept that demonstrates the feasibility and benefits of the proposed solution.

Applicants should:

• Have a keen interest in machine learning, knowledge management, and educational technology.
• Be capable of working independently, with a proactive and problem-solving attitude.
• Possess strong communication skills and be willing to collaborate with a research team.
• Demonstrate proficiency in scientific writing in English.
• Have experience with programming languages such as Python, which is crucial for ML model development.

• Close supervision by experienced researchers in the fields of Information Systems, AI, and ML, ensuring comprehensive guidance throughout the project.
• Regular review meetings and feedback sessions to support your progress and development.
• The opportunity to contribute to cutting-edge research with the potential for publication in renowned conferences or journals.
• A flexible start date, with a completion timeline of approximately 6 months.

Interested candidates are invited to apply by submitting the following documents:

• A CV outlining relevant experience and skills.
• A transcript of records demonstrating academic performance.
• An estimated timeline for the thesis, including milestones and deliverables.
• A motivation letter (max. 250 words) explaining your interest in the topic and how you meet the candidate profile.