See the job offer

France Energies Marines; Brest; Sur place; EUR 35 000 - 50 000; Plein temps

Résumé du poste

Une entreprise pionnière dans l'énergie marine cherche un candidat pour participer à un projet de R&D en quantification des incertitudes climatiques. Le candidat idéal aura un diplôme en Mathématiques Appliquées ou en Sciences et devra travailler sur des méthodologies pour traiter les incertitudes dans les projets d'énergie éolienne offshore. Ce poste exige des compétences en statistiques et en programmation, et offre une collaboration avec des chercheurs d'autres instituts.

Qualifications

• Formation initiale Master ou diplôme d’ingénieur en Mathématiques Appliquées ou Sciences Climat.
• Première expérience dans la gestion de données climatiques recommandée.
• Capacité à travailler en groupe dans une approche multidisciplinaire.

Responsabilités

• Implémenter un cadre statistique basé sur l’analyse de variance pour estimer l’incertitude totale.
• Explorer des outils avancés de science des données comme l'assimilation de données.
• Tester les méthodologies développées avec de nouveaux ensembles de données climatiques.

Connaissances

Rigueur

Curiosité scientifique

Autonomie

Communication

Formation

Master ou diplôme d'ingénieur en Mathématiques Appliquées ou Sciences du Climat

Outils

Python

R

Description du poste

A key challenge in climate change impact assessments lies in quantifying the uncertainties associated with future projections and identifying the relative contributions of various sources of uncertainty. Uncertainty modeling of the climate system stems from multiple factors, including internal variability, model uncertainty, and scenario uncertainty. These challenges are evident in the performance of General Circulation Models (GCMs), which are used to simulate and characterize future climate behavior (Abdulai and Chung, 2019 ). Internal variability refers to the natural fluctuations within the climate system that occur independently of external radiative forcing (Marotske and Forster, 2015 ). Prominent examples of internal variability modes include the El Niño–Southern Oscillation, the North Atlantic Oscillation, and the Pacific Decadal Oscillation, all of which significantly influence regional and global climate patterns. Model uncertainty, also known as response uncertainty, arises from differences in climate projections produced by various models under the same forcing scenarios. This type of uncertainty stems from limitations in model structure and the parameterizations used to represent complex geophysical processes. Scenario uncertainty is associated with the unpredictability of future greenhouse gas emissions, which depend on socio-economic developments, policy decisions, and technological advancements. This uncertainty reflects the range of possible future pathways that humanity might follow.

The development of an uncertainty quantification framework aimed at enhancing the clarification of all these sources of uncertainties in climate change projections is essential for producing more accurate and reliable predictions that can be used by the Offshore Wind Industry.

Missions

The successful candidate will:

• implement a statistical framework based on analysis of variance (ANOVA, Hingray et al., 2014) to estimate total uncertainty and attribute it to specific sources. This approach will leverage outputs from CMIP6 and incorporate multi-model, multi-scenario experiments. Additionally, the use of “storylines” will help reduce complexity by focusing on a limited set of plausible future trajectories, aiding stakeholder engagement and adaptation planning.
• explore advanced data science tools, such as data assimilation and model weighting techniques (e.g., Bayesian Model Averaging, Reliability Ensemble Averaging; Ruiz et al., 2022). These methods compare model outputs with observations to assign weights and reduce projection uncertainty.

As part of a collaborative R&D project, the successful candidate will work with partners from other research institutes to:

• test the developed methodologies with new climate datasets;
• apply the results to offshore wind applications: wind energy production (using wind speed and associated uncertainties), turbine design (using wind/wave parameters and uncertainties), and landfall zones (using extreme wave events and uncertainties). A key objective is to propagate the quantified uncertainties from climate projections into these application domains to support performance and design in the future.

Required Skills

Initial training

Master or Engineer’s degree in Applied Mathematics or Ocean/Weather/Climate Science

A first experience in climate datasets management is recommended

• Rigor and scientific curiosity
• Autonomy, organization and proactive
• Ease of expression, argumentation and communication in a partnership context
• Ease of writing (scientific papers and thesis)
• Ability to work in a group in a multidisciplinary approach

Specific knowledge

• Strong background in applied mathematics (statistics and/or machine learning)
• Proficiency in programming languages (Python or R)
• French and English level B1 (or equivalent)

Would be a +:

• Knowledge of Wind Energy
• Uncertainty quantification

WARNING: If you are unable to submit your application via our website, please send it by e-mail [e-mail] , specifying the job reference in the subject field.