Context & Environment

Black-box optimization is a discipline where one seeks to solve a problem without knowing the analytical form or derivatives of the objective function or a family of constraints. Evaluations of the function or constraints are made solely through observations of inputs and outputs, without access to the system's internal mechanisms. In the energy sector, it is common to encounter undefined objective functions, obtained through simulators that model complex phenomena for which it is difficult to derive precise analytical expressions. A typical example is the optimization of wind farm layouts, where the goal is to maximize energy production while minimizing costs and environmental impacts. To achieve this, it is necessary to simulate wake effects and interactions between wind turbines. Wake effects occur when wind passes through a turbine, creating turbulence zones that can affect the performance of downstream turbines. These complex interactions are generally modeled using sophisticated simulators, making wind farm layout optimization a typical black-box problem.

Activities

As a Application of Black-Box Optimization in the Energy Sector M/F trainee, your missions will be :

• Conduct a literature review on black-box optimization: Research, analyze, and identify current trends and challenges based on scientific publications and existing work on black-box optimization.
• Define the most effective methods proposed to solve optimization problems with a black-box objective: Examine and compare different approaches and algorithms used for black-box optimization.
• Classification of methods by use case: Categorize optimization methods based on their application domain and use cases.
• Implementation of a method for the Wind Farm Layout Optimization use case: Select and develop an optimization method suitable for wind farm layout optimization in Python.
• Testing, validation, and writing a deliverable: Test and validate the algorithm on real or simulated data to evaluate its performance. Write a detailed report.

You will evolve within a team of experienced professionals and with a tutor-coach, the reference for your future profession. Individualized support will help you develop your autonomy and lead you to your diploma!

Candidate Profile

Currently enrolled in an engineering school or Master's program in the Research and development field, are you looking for an 6-month end-of-study internship starting in March 2025?

Do you have experience in skills in operations research and nonlinear optimization and knowledge of heuristics and metaheuristics ? Do you know Python for algorithm development ?

Are you comfortable with office automation and familiar with the Office suite? You'll be using Excel, Word and Powerpoint in particular. Skills in using solvers (Gurobi, Cplex, etc.) will be appreciate.

Are you a self-starter, rigorous and a team player? Can you take the initiative? A p assion for renewable energy and sustainability is a plus!

A professional command of English will be essential for this position.

So don't wait any longer, apply to join our team!

Additional Information

Cette offre concerne un stage conventionné à temp plein, les stages alternés ne sont possibles.
Pour postuler, merci de joindre un CV + lettre de motivation (vos dates de stage doivent être indiquées).

This offer is for a full-time, contractual internship; alternating internships are not possible.

To apply, please attach a CV + covering letter (your internship dates must be indicated).

TotalEnergies values diversity, promotes individual growth and offers equal opportunity careers.

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TotalEnergies is a global multi-energy production and supply company: oil and biofuels, natural gas and green gas, renewables and electricity. Its 105,000 employees are committed to making energy ever more affordable, clean, reliable and accessible to as many people as possible. Present in more than 130 countries, TotalEnergies places sustainable development in all its dimensions at the heart of its projects and operations to contribute to the well-being of populations.