Internship - ML Research

Key Skills

Aartificial intelligence, Deep Learning, Machine learning techniques, PyTorch, Software Development, TensorFlow

Job Description

You are towards your final years of a PhD programme in Machine Learning/Statistics/Computer Vision/NLP, and have already published some of your results in a main conference in the field. You will hone your research skills with us, as we go through the various collaborative phases of an ML research project: identify a promising research opportunity, survey SoTA methods and relevant literature, imagine and design novel methods, implement them as code prototypes, plan and complete experiments at multi-node, multi-GPU scales, write a paper and follow through with a submission. 

Topics of interest include but are not limited to differentiable optimization (e.g. bi- and multi-level programming), generative modelling (diffusions, transport) and uncertainty quantification (conformal prediction, calibration). You’ll also have the opportunity to collaborate further with MLR colleagues outside of Paris on the project. Ultimately, you will work towards publishing new findings arising from the project, either or both as open source code and publications. 

Minimum Qualifications

• Students currently pursuing a MSc in Computer Science or Mathematics, with a specialisation in ML, and very strong coding skills as demonstrated by participations in open source projects may also apply.
• Demonstrated expertise in machine learning research.
• Publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR, ICCV, ECCV, ACL, EMNLP, etc).
• Hands-on experience working with deep learning toolkits such as JAX or PyTorch.

Preferred Qualifications

• Ability to work in a diverse collaborative environment.
• Strong mathematical skills in linear algebra, probability, optimization and statistics.
• Strong coding skills.
• Ability to formulate a research problem, design, experiment, implement and communicate solutions.