Research Internships (W/M)

🔎 Who are we?

Kyutai is a non-profit research laboratory dedicated to open science in artificial intelligence. Our team focuses on high-impact projects at the frontier of current knowledge in AI, and on training young researchers through internships and the supervision of doctoral students and post-docs.

📝 Internship description:

As a research intern, you will be part of a team of experienced researchers and will actively participate in the advancement of our work in AI. Your main missions will be as follows:

1. Definition of the internship subject: in collaboration with your supervisor, you will participate in defining a precise research subject according to your profile, your interests and the research priorities of the laboratory, subject which must be validated by your school or university.

2. Literature review: you will carry out an in-depth study of the state of the art on the chosen subject. This will involve reading scientific articles, understanding existing techniques and potentially reproducing certain experimental results.

3. Formulation of hypotheses: you will critically analyze the state of the art, you will generate new hypotheses and, in conjunction with your supervisor, you will formulate clear and testable research hypotheses, specifying the expected results.

4. Development of models and methods: you will then explore new models or new learning techniques, through the design of the architecture of the model and its optimization algorithm, its training with suitable datasets (to be built if necessary) and , finally, its evaluation by calculating performance metrics on existing benchmarks and comparing it to baselines and alternative models. Software development and experiments will be conducted according to the best practices in the field.

5. Publication of results: where applicable, you will contribute to the publication of the results of your research by writing a research article or paper (and potentially submitting it to scientific conferences), and to the publication of code on platforms such as GitHub or Hugging Face.

🎓 About you

• Student in Master 2 or in the last year of engineering school or university with a specialization in applied mathematics, computer science and/or machine learning.

• Solid knowledge of mathematics and algorithms.

• Knowledge of the fundamental concepts of machine learning (machine learning) and deep learning (deep learning), concepts ideally applied in computer vision, audio signal processing, speech processing or natural language processing.

• Experience in using machine learning frameworks (PyTorch, Jax or TensorFlow).

• Mastery of a programming language, such as Python, C++, C, Java or Rust.

• Intellectual curiosity, scientific rigor and team spirit.