You are in your final years of a PhD programme in Machine Learning Statistics Computer Vision or NLP and have already published some of your work at major conferences in the field. During your time with us you will continue sharpening your research skills as we go through the various collaborative stages of an ML research project. Identifying a promising research opportunity reviewing SoTA methods and relevant literature crafting novel approaches implementing them as code prototypes planning and running large-scale experiments across multi-node multi-GPU systems writing a paper and seeing it through to submission. Topics of interest include but are not limited to generative modelling (diffusions discrete diffusions flows transport) optimization (e.g. bi- and multi-level programming scaling laws for NN training parameterization) uncertainty quantification data-centric ML (curriculum learning data reweighting). Youll 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.
- Students currently pursuing a MSc or a PhD 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.
- Proven expertise in machine learning research.
- Publication record in relevant conferences (e.g. NeurIPS ICML ICLR AISTATS CVPR ICCV ECCV ACL EMNLP etc).
- Hands-on experience working with deep learning toolkits such as JAX or PyTorch.
- 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
Required Experience:
Intern
You are in your final years of a PhD programme in Machine Learning Statistics Computer Vision or NLP and have already published some of your work at major conferences in the field. During your time with us you will continue sharpening your research skills as we go through the various collaborative s...
You are in your final years of a PhD programme in Machine Learning Statistics Computer Vision or NLP and have already published some of your work at major conferences in the field. During your time with us you will continue sharpening your research skills as we go through the various collaborative stages of an ML research project. Identifying a promising research opportunity reviewing SoTA methods and relevant literature crafting novel approaches implementing them as code prototypes planning and running large-scale experiments across multi-node multi-GPU systems writing a paper and seeing it through to submission. Topics of interest include but are not limited to generative modelling (diffusions discrete diffusions flows transport) optimization (e.g. bi- and multi-level programming scaling laws for NN training parameterization) uncertainty quantification data-centric ML (curriculum learning data reweighting). Youll 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.
- Students currently pursuing a MSc or a PhD 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.
- Proven expertise in machine learning research.
- Publication record in relevant conferences (e.g. NeurIPS ICML ICLR AISTATS CVPR ICCV ECCV ACL EMNLP etc).
- Hands-on experience working with deep learning toolkits such as JAX or PyTorch.
- 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
Required Experience:
Intern
View more
View less