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Job Description
In the era of rapidly advancing artificial intelligence, the project aims to bridge the gap between human language understanding and mathematical problem formulation. This project leverages the power of large language models to transform natural language data into basic mathematical programming formulations, specifically focusing on linear programming problems (mainly in the field of supply chain and logistical) with restricted variables and constraints.
Qualifications
- Holding a Bachelor’s degree in Computer Science, Industrial Engineering, Mathematics, or other related Engineering or Science disciplines.
- Background in Natural Language Processing (NLP): Candidates should have some understanding of machine learning principles, particularly in the field of NLP. The candidate should be familiar with transformer architectures, such as BERT, GPT, or T5, and have experience in fine-tuning and training these models on large datasets.
- Proficiency in Mathematical Optimization: Prior exposure to understanding of mathematical optimization techniques, especially linear programming, is essential. The candidate should be proficient in formulating optimization problems, interpreting constraints, and defining objective functions.
- Programming Skills: Strong programming skills are preferred, particularly in languages commonly used in machine learning and NLP, such as Python. Candidates should be adept at libraries and frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and Scikit-learn.
