Open Internship Proposals

We are looking for students finishing their MSc with a solid background in computer vision and machine learning, particularly in deep learning with strong PyTorch coding skills. Interns work on research topics, typically for 6 months (usually spring and summer), resulting for a great part in paper submissions to top-tier conferences. Some trainees go on to do a PhD thesis in the lab. We currently have four exciting internship opportunities for MSc students!

How to Apply

Send an email to the supervisors (one email per application) with the following:

• A cover letter explaining your interest and qualifications for the topic.
• Your CV/resume.
• Transcripts of your grades from last year (and this year, if available).

Available Topics:

Scoring Trajectories with Vision Language Models
Keywords: Vision-Language Models (VLM), Trajectory Evaluation, Autonomous Driving, Deep Learning
Supervisors: Monika Wysoczanska, Ellington Kirby, Alexandre Boulch
Investigating the capacity of Vision-Language Models (VLM) to evaluate the quality of a vehicle trajectory given the input images.

LiDAR Video Generation from Range Images
Keywords: Diffusion Models, Generative AI, LiDAR, Range Images, Video Synthesis
Supervisors: Nermin Samet, Victor Besnier
Developing a diffusion-based generative model that produces temporally consistent and controllable LiDAR video scenes in the range image domain.

Unified Vision Encoders: Multi-Teacher Distillation and Dynamic Transformers
Keywords: Knowledge Distillation, Vision Transformers, Dynamic Computation, Computer Vision, Representation Learning
Supervisors: Spyros Gidaris, Shashanka Venkataramanan, Gilles Puy
Distill efficiently multiple teachers into a single vision transformer augmented with dynamically adaptive computation.

Unconstrained Dynamic Novel View Synthesis
Keywords: Novel View Synthesis, Transformers, 3D Computer Vision, Dynamic Scenes, Data-Driven Approaches
Supervisors: Anh-Quan Cao, Tuan-Hung Vu, Yihong Xu
Explore transformer-based, fully data-driven approaches for scalable and generalizable dynamic novel view synthesis from unconstrained views.

Alumni interns and visiting students

Projects led or contributed to by interns