M2 Data Science | Deep Learning & Generative Models
Seeking a 6-month internship in Data Science / Machine Learning
- Deep Learning : DNN, RNN, Transformers
- Machine learning : Linear Regression, Logistic Regression, kNN, k-Means, Hierarchical Clustering, GMM, SVM, Random Forests, XGBoost
- Optimization for Data Science : GD, AGD, PGD, APGD, SGD, BFGS
- Reinforcement Learning basics
- Generative Models basics
- Natural Language Processing & Sentiment Analysis
- AI for Sound: Analysis, Processing & Generation
Reproduction (simplified) of the paper:
"How to Build a Consistency Model: Learning Flow Maps via Self-Distillation"
Implemented:
- Self-distillation training loop
- Flow-map consistency objective
- Sampling comparison
Focus areas:
- Diffusion-based generative modeling
- Continuous-time formulation
- Numerical stability considerations
- Experimental validation
Stack:
- PyTorch
- Mathematical modeling
- Research paper implementation
🔗 Repository: https://github.com/NathanVanier/flow-map-self-distillation
Python, SQL, java
scikit-learn, PyTorch, pandas, numpy
RNN, Attention, Seq2Seq, Diffusion Models, Consistency Models
Git, Linux, Jupyter
Looking for a 6-month internship starting <01/04/2026 date> where I can contribute to:
- Model development and evaluation
- Research implementation
- Generative AI or NLP systems
- End-to-end ML pipelines