Master Thesis, Semester project
Master Thesis, Semester project
Ref. 2024_019
Project description
We are seeking a Master’s student (or recent graduate) to join an exciting research project at the intersection of Natural Language Processing (NLP) and data management systems. This project aims to enhance the planning capabilities of current Large Language Models (LLMs) to generate and optimize query execution plans for efficiently retrieving data.
As part of a larger effort to advance automated formal language generation, this role offers an excellent opportunity to contribute to our research and publish work.
Key Responsibilities:
- Data Curation: Curate and develop a large instruction dataset for Q&A on databases using both open-access and synthetically generated data.
- Model Fine-Tuning: Fine-tune LLMs through supervised learning and reinforcement learning techniques, targeting improved planning and decision-making skills.
- Performance Evaluation: Evaluate the accuracy and efficiency of the fine-tuned models on large-scale data management systems.
This project offers an opportunity to work at the crossroads of NLP, machine learning, and database management, contributing to both academic research and practical advancements in query optimization and data retrieval.
Qualifications:
- Current Master’s student (or recent graduate) in Computer Science, Engineering, or a related field.
- Strong understanding of LLM fine-tuning and experience with machine learning frameworks such as PyTorch.
- Proficient in Python and familiar with Git for version control.
- Prior experience or knowledge of Database Management Systems (DBMS) is preferred.
Diversity
IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.
How to apply
Please submit your application through the link below. This position is available starting immediately or at a later date.
