Post-doctoral Researcher in Coastal Dynamics

University of Bordeaux

Bordeaux, France

€ 2,750 - € 3,090 per month gross

Your mission

Your mission is part of Work Package 3 of the IRICOT project and aims to better understand the links between climate variability and coastal risks on the French Atlantic and Mediterranean coasts. You will mainly work on identifying and diagnosing the relationships between climate variability modes and indicators of the coastal system. You will mobilize and cross-reference retrospective simulations and observational data to characterize the physical and statistical mechanisms underlying these relationships.

Your mission will be to identify and characterize the links between climate variability modes and coastal hazards on the Atlantic and Mediterranean coasts. You will contribute to the production of scientific results, to their promotion in publications and conferences and to the collaborative work dynamic with the various project partners and the two doctoral students involved in the WP3 of the project.

Bibliographic synthesis and scientific framing

• You carry out a literature review on climate variability at seasonal to decadal scales and its impacts on coastal systems, with a particular focus on variations in water levels and their main forcing;
• You identify the main processes, mechanisms, and indicators at stake for the French Atlantic and Mediterranean coasts, in connection with societal needs.

Analysis of water levels and observation data

• You characterize the seasonal, interannual to decadal variability of water levels on the Atlantic and Mediterranean facades, based on different sources: tide gauges, retrospective simulations, satellite altimetry, etc.;
• You assess the complementarity, strengths, limitations, and uncertainties of different data sources and develop appropriate approaches for their integration.

Identification of climate forcings and links with coastal indicators

• You analyze the relationships between water levels, climate variability modes (NAO, EA, SCAND, WEPA/WEMO, etc.), and marine weather conditions, distinguishing between winter and summer specificities;
• If necessary, you identify new climate indices or combinations of indices that can better explain the observed variability, and explore their links with other environmental indicators from the SOMLIT and COAST-HF networks in particular.

Interdisciplinary work and contribution to climate services

• You interact closely with climate scientists and project partners in order to confront the identified mechanisms with the predictability capabilities of climate indices and to direct analyzes towards high-impact issues;
• You contribute to the scientific advancement and development of future climate services dedicated to anticipating coastal risks on the Atlantic and Mediterranean coasts.

Your strengths/Your talents

Holder of a PhD in the field of coastal dynamics, oceanography, climate science, environmental science, or in a related field, you have at least 2 years of experience and developed strong skills in physical oceanography, dynamics and coastal processes, and/or in atmospheric science.

• You are autonomous, rigorous, and endowed with a sense of organization while knowing how to take initiative, solicit, and integrate the contributions of other team members;
• You demonstrate excellent reasoning, analysis, synthesis, and complex problem-solving skills;
• You are recognized for your scientific communication skills, both written and oral, and your ability to present and discuss results with different scientific audiences;
• You are proficient in programming languages ​​and scientific data processing, notably with Python R, and/or MATLAB (or other);
• You are familiar with concepts related to climate variability, modes of large-scale variability, and remote connections;
• You have strong skills in statistics and data analysis, including time series analysis, identifying trends, and characterizing variability;
• You are able to cross-reference, integrate, and synthesize large volumes of heterogeneous data from different sources: observations, satellites, reanalyses, models, etc.;
• Experience in the development of reproducible analysis methods and in scientific data visualization would be an asset.