INTERNSHIP – MACHINE LEARNING

Internship
Marseille, Paris
Publié il y a 7 mois

Context

It is challenging in many ways to develop a net zero electricity grid. Adding renewable energies means more need for storage and for grid balancing. Grid-scale batteries handle these issues but their operation is not quite simple.

A battery has access to many different revenue streams, each being specific. No revenue alone is enough to make it economically viable. All revenues stacked and optimized together ensure profitability in the long run and a fast deployment.

We are developing optimization and trading algorithms that merge the battery revenue streams and ensure their sustainable development. Their deployment will form an autopilot that makes the best decisions, in real time, on the electricity markets to maximize the batteries profit and lifetime.

About us

StackEase is a deeptech spinoff from the INRIA (French Institute for Research in Computer Science). It secured its first fundings and is enrolled with a world-class accelerator, starting this fall. Members are located in Paris, Lyon and Marseille.

Our values are innovation, customer satisfaction, merit and sustainability. The company’s purpose is to leverage Machine Learning to accelerate the energy transition.

Missions

  • Develop prediction tools for electricity market prices through supervised learning
  • Develop Reinforcement Learning based trading and optimisation algorithms for grid-scale battery profit optimisation
  • Develop automated unit tests and non regression tests
  • Validate and document results on real case scenarios
  • Deploy the algorithms on the cloud

Preferred Skills

  • Enrolled in the last year of master degree of computer science, applied mathematics or electrical engineering
  • Excellent skills in mathematics, optimisation and computer science
  • Practical knowledge of supervised and reinforcement Learning
  • Enthusiastic, rigorous and autonomous, looking to discover entrepreneurship and the renewable energies
  • Fluent in English, proficient in French

Compensation

  • Paid internship
  • Partial home office possible

Caractéristiques de l'emploi

Catégorie emploi

Full Time

Start Date

Flexible

Job ID

2023S001

Duration

4 to 6 months

Postuler en ligne

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