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PhD Position in Code and Proof Generation with Large Language Models
il y a 1 mois
Job Opportunity:
We are seeking a highly motivated PhD researcher to join our team at INRIA and explore new research in neural code generation and applications to formal verification.
About the Project:
Generative AI has gained significant interest in tackling problems from linguistics, maths, commonsense reasoning, biology, physics, etc. Transformers have become the state-of-the-art neural network architecture for sequence processing with applications ranging from natural language processing and computer vision to code generation. Large Language Models (LLMs) like GPT-4 are extremely large Transformers models trained for natural processing tasks on huge datasets containing billions of words.
Research Objectives:
In this thesis, we will explore new research in neural code generation and applications to formal verification. We aim to improve the reliability of LLM-based code assistants by exploring possible interactions between the LLM and external tools like a Python interpreter, a test framework, or a proof assistant. We will also focus on building a tool to enable the triple interaction human-ITP-LLM for Coq.
Responsibilities:
The successful candidate will be responsible for conducting research in neural code generation and formal verification, developing new techniques and tools, and collaborating with the team to achieve the project objectives.
Requirements:
We are looking for a highly motivated and talented researcher with a strong background in computer science, mathematics, or a related field. The ideal candidate should have a solid understanding of machine learning, programming languages, and formal verification.
What We Offer:
We offer a competitive salary, a dynamic and collaborative work environment, and opportunities for professional growth and development. We also provide access to state-of-the-art facilities and resources, including a team of experienced researchers and engineers.
How to Apply:
Interested candidates should submit their application, including a CV, a cover letter, and contact information for at least two academic or professional references. We look forward to receiving your application.
References:
- [1] Attention Is All You Need, Vaswani et al., 2017
- [2] Evaluating Large Language Models Trained on Code, Chen et al., 2021
- [3] Training Compute-Optimal Large Language Models, Hoffmann et al., 2022
- [4] Direct Preference Optimization: Your Language Model is Secretly a Reward Model, Rafailov et al., 2023
- [5] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Lewis et al., 2020
- [6] Competition-Level Code Generation with AlphaCode, Li et al., 2022
- [7] StarCoder: may the source be with you, Li et al., 2023
- [8] CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation, Wang et al., 2021
- [9] Code Llama: Open Foundation Models for Code, Rozière et al., 2023
Benefits:
- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking
- Flexible organization of working hours (after 12 months of employment)
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage
