PhD Position F/M Enforcing Persistence in Generative World Models for Robotics

Inria Paris; Paris; On-site; EUR 20,000 - 26,000; Full time

Job summary

Inria Paris invites applications for a doctoral research position in AI-driven robotics. The candidate will conduct research on persistence in generative world models, develop algorithms that integrate physical concepts in image generation, and travel for dissemination and training activities.

The project is a collaboration with Telecom ParisTech and PRAIRIE clusters, offering exposure to cutting-edge AI research and daily supervision by senior researchers at Inria Paris and Telecom ParisTech.

Qualifications

• Proficiency in English (written and spoken).
• French is strongly recommended.
• Background in AI and computer vision is beneficial.

Responsibilities

• Conducts research and software development under supervision.
• Publishes scientific articles and reports.
• Contributes to dissemination of scientific knowledge.
• Participates in team activities and collaborations.

Skills

Python

PyTorch

Git

Linux

GPU computing

Data processing

Mathematical background

Education

PhD student / PhD candidate

Tools

Stable Diffusion

ONNX

Job description

Context

AI4IDF is a structuring and unifying research project dedicated to artificial intelligence, a scientific and technological field that has become essential.

Aiming to deepen knowledge in AI while keeping human beings at the center of its concerns, the Ile-de-France Region aspires to play a major role in this promising sector, through its scientific excellence and its incomparable ecosystem, and wishes to make the latter a key player in building a frugal, reliable, and efficient Artificial Intelligence (AI), designed to coexist with humans and assist them in their decisions.

To this end, DIM AI4IDF periodically launches calls for doctoral research projects. This thesis falls within this framework and establishes a partnership between the Hi!Paris and PRAIRIE clusters, specifically between Telecom ParisTech and Inria Paris Centre.

The objective of this thesis is to conduct joint research on persistence in generative world models for robotics and to develop algorithms capable of integrating physical concepts during image generation in particular. The project will develop methods to enforce persistence in generative world models, with contributions including a formal definition of persistence, trajectory-level learning methods, and empirical validation in robotic prediction and control.

Regular travel is expected for this position, notably for dissemination purposes, scientific training, and participation in project and collaboration meetings.

Assignment

Responsibilities

Under the guidance of researchers from Inria and Telecom ParisTech, the successful PhD candidate will carry out scientific research and software development under the direct supervision of their supervisors.

Main Activities

• Conducting research and development
• Writing scientific articles and reports
• Contribute to the dissemination and popularisation of scientific knowledge
• Undertake scientific and professional development training
• Participate in the activities of their research teams and in events organised by their laboratories

Other activities

• Participate in contractual activities and collaborative projects undertaken by their respective teams.

Skills

Technical skills and level required:

Languages: proficiency in English (written and spoken) is required. French is strongly recommended.

Interpersonal skills: being open to multicultural and inclusive research environments and adhering to the social and professional code of conduct established in the host laboratories. Respecting professional and conduct rules as well as research ethics is required.

Additional skills appreciated: proficiency in tools, languages, and development libraries for computer vision and AI development. The essential combination is Python, PyTorch, Git (Linux is a plus), complemented by skills in GPU computing, large-scale data processing, and a solid mathematical background.

Specifically for the subject of this thesis, knowledge in the following areas is strongly recommended:

Generative models

• diffusion (Stable Diffusion, DDPM)
• flows
• VAE

Frugal AI

• quantization
• pruning
• distillation
• edge computing (TensorRT, ONNX)

Collaboration

The successful candidate will be linked to the research and training ecosystems of Telecom ParisTech and the Paris centre of Inria. In particular, the Multimedia (MM) laboratory at Telecom ParisTech (*) and the ASTRA project team at Inria Paris (**).

* https://www.telecom-paris.fr/en/research/labs/information-processing-ltci/teams/multimedia

** https://astra-team.github.io