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Veolia

Water, waste, and energy services provider

Data Science Intern

Data ScienceConfirmed live in the last 24 hours
InternshipAubervilliers, FranceNo salary listedIn-person

About the Job

Requirements

  • Be seeking a final-year internship from an engineering school or university.
  • Have strong software development skills in backend and frontend development.
  • Have strong knowledge of statistics, data analysis, and data visualization.
  • Have strong programming skills in Python.
  • Know machine learning and deep learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
  • Have an interest in mathematics, physics, and the theory of sampling solid particles in mixtures.
  • Be able to explain complex concepts clearly and work in a multidisciplinary team.
  • Have strong written and oral communication skills.
  • Have French proficiency at C1 level.
  • Have operational English proficiency at B1/B2 level.

Responsibilities

  • Develop, stabilize, and deploy version 1 of the solid-particle mixture sampling application and evolve the partially completed version 0.
  • Train artificial intelligence models.
  • Enrich artificial intelligence modules and connect them to the frontend application.
  • Apply the simulator to other deposits.
  • Develop a module for sizing operations across the entire sampling chain.
  • Support multimodal deposits.
  • Conduct a literature review of sampling methods, generative models such as generative adversarial networks and variational autoencoders, and suitable data visualization tools.
  • Learn and apply Gy's theory for sampling solid materials and understand its industrial implications.
  • Design, evaluate, and validate a mathematical model that simulates the impact of sampling parameters on measurement error.
  • Explore and implement generative models such as generative adversarial networks and variational autoencoders to simulate realistic particle distributions in deposits and create varied, representative sampling scenarios.
  • Evolve the existing Python simulator to integrate the models, test sampling scenarios, optimize protocols, and provide an interface for selecting parameters and presenting results clearly.
  • Develop an intelligent recommendation model using machine learning techniques to automatically suggest optimal sampling parameters based on deposit characteristics.
  • Implement explainability techniques such as SHAP and LIME to analyze and visualize each parameter's impact on results.
  • Present the completed work through a report, presentations, tool demonstrations, and a user manual.

Desired Qualifications

  • Experience or interest in generative models such as generative adversarial networks and variational autoencoders.
  • Familiarity with model explainability techniques such as SHAP and LIME.

About the Company

Veolia

Water, waste, and energy services provider

Generating company summary.

Company size
10,001+
Company stage
IPO
Headquarters
Aubervilliers, France
Founded
1853

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