Deep-Learning Weather Prediction Engineer (H/F)

TotalEnergies Group; Palaiseau; Hybride; EUR 60 000 - 80 000; Plein temps

Résumé du poste

TOTAL Deutschland GmbH seeks a seasoned professional for its Integrated Power Modeling initiative focusing on advanced weather forecasting solutions. The candidate will support commercial ambitions, manage relevant datasets, and contribute to deep learning model development.

The ideal candidate holds a Master's or PhD in deep learning, possesses extensive experience, and is proficient in cloud and HPC infrastructures. Proficiency in English and French is required, and a strong commitment to collaboration and knowledge sharing is essential.

Qualifications

• More than 6 years of professional experience.
• Hands-on knowledge of deep learning-based weather forecasting models.
• Proficiency in computing infrastructures.

Responsabilités

• Support commercial ambitions in weather forecasting.
• Collect relevant datasets tailored to geographical areas.
• Design and develop robust internal workflows based on deep learning solutions.
• Conduct industrial and academic monitoring for weather forecasting technologies.

Connaissances

Deep learning

Weather forecasting

Data collection

Model evaluation

Stakeholder collaboration

Formation

Master's or PhD in deep learning with specialization in meteorological modeling

Outils

Cloud computing infrastructures

High-Performance Computing (HPC)

Storage architectures

Description du poste

TotalEnergies has launched an ambitious Integrated Power Modeling initiative designed to secure a leading position in next-generation energy systems and electricity markets.

As renewable energy sources are increasingly integrated into power grids and industries, transportation, and buildings undergo large-scale electrification, the energy landscape is evolving at an unprecedented pace.

In this context, advanced modeling and forecasting solutions to support supply-demand balance management have become critical enablers of strategic decision-making and long-term competitiveness.

Activities

• Support short- and medium-term commercial ambitions in weather forecasting
• Collect and acquire the most relevant datasets both open-source and proprietary tailored to specific geographical areas
• Design and develop robust internal workflows based on deep learning solutions
• Continuously integrate and evaluate the latest models from major tech companies, startups, and academia
• Create educational content adapted for a general audience
• Produce scientific documentation to explain internal developments and contribute to scientific conferences
• Provide support for the adoption and use of internal forecasting tools within the organization
• Conduct both industrial and academic monitoring to stay at the forefront of weather forecasting technologies
• Identify and collaborate with stakeholders at all levels: academia, industry, business units, and programs
• Contribute to team skill development through training, workshops, and knowledge-sharing initiatives

Candidate Profile

• Master's or PhD in deep learning with a specialization in meteorological modeling.
• Hands‑on knowledge of deep learning‑based weather forecasting models such as GraphCast, Aurora, and FourCastNet.
• Hands‑on knowledge of the most relevant input data for weather forecasting: meteorological agencies, satellite imagery, and local observations.
• Practical knowledge of the prerequisites for industrializing deep learning.
• Proficiency in computing infrastructures, both cloud and HPC including Slurm, as well as storage architectures.
• More than 6 years of professional experience.
• English and French required.

Additional Information

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