Stage - Data Science : Développement d’un Simulateur d’Échantillonnage Avancé pour l’optimisation des mesures sur matières solides - H/F
Core
Develop an advanced sampling simulator using Gy theory and generative AI to optimize measurement protocols for heterogeneous solid materials.
Role type
Research intern (Data Science & Simulation)
Builds
A Python-based sampling simulator with AI features for industrial material analysis
Domain
Environmental science / Industrial data science / Solid waste management
Deliverable
production ML models | product features
Required skills
Python, statistics, data visualization, machine learning, deep learning, generative models (GANs, VAE), model explainability (SHAP, LIME)
Preferred skills
Gy sampling theory, mathematical modeling, physics
Technologies
Python, scikit-learn, TensorFlow, PyTorch
Responsibilities
Evolve an existing sampling simulator by integrating AI models and frontend connectivity; design mathematical models to simulate sampling impact on measurement error; implement generative models to create realistic particle distribution scenarios; develop an intelligent recommendation model for optimal sampling parameters; implement explainability techniques to visualize parameter impacts; conduct literature reviews on sampling methods and generative models.
Seniority
Intern (6 months)
