Research Internship on Latent Variable Models for NLP (VAEs/Diffusion)

About the role

The RCLN team is offering a research internship focused on advancing latent‑variable models for natural language processing. Interns will explore classic approaches through modern techniques such as variational autoencoders (VAEs) and diffusion processes, contributing to cutting‑edge ML research.

Key responsibilities

• Conduct literature reviews on latent‑variable methods and recent VAE/diffusion advancements.
• Design, implement, and evaluate VAE and diffusion‑based models for NLP tasks.
• Run experiments, analyze results, and document findings.
• Prepare technical reports and present progress to the research team.

Required profile

• Current graduate student or recent graduate in computer science, machine learning, or a related field.
• Strong interest in research and ability to work independently.
• Good written and verbal communication skills in English.

Required skills

• Python programming.
• Experience with deep‑learning frameworks such as PyTorch or TensorFlow.
• Fundamental knowledge of machine learning and natural language processing.
• Understanding of variational autoencoders and diffusion processes is a plus.

What we offer

• Hands‑on research experience in a leading academic lab.
• Mentorship from experienced researchers.
• Potential co‑authorship on scientific publications.
• Flexible working schedule.