Marine Ecophysiology Postdoc - Multi-Stressor Experiments

CNRS - National Center for Scientific Research; France; On-site; EUR 36,000 - 48,000; Full time

Job summary

The postdoctoral researcher will join the Chemistry, Ocean and Climate (CHOC) research team at the Laboratoire d'Océanographie de Villefranche (LOV), CNRS, Villefranche-sur-Mer, France. The project SAF-SEA examines how marine organisms respond to multiple stressors (pH, temperature, oxygen, salinity) and aims to harmonize experimental approaches across European partners.

Responsibilities include designing innovative experiments, analyzing environmental time series, and contributing to

Qualifications

• PhD in oceanography, marine biology or a closely related field.
• Experience designing and conducting experiments with multiple stressors.
• Strong data analysis skills using R and/or Python.

Responsibilities

• Design and conduct single- and multiple-stressor experiments on marine organisms and communities.
• Analyse environmental time series to define experimental conditions.
• Perform carbonate chemistry measurements and calculations.
• Coordinate joint experiments with European project partners and ensure data comparability.
• Disseminate findings through publications and conference presentations.
• Supervise students and contribute to project deliverables.

Skills

Marine ecophysiology

Experimental marine ecology

Statistical analysis

Time series analysis

English proficiency (C1–C2)

Independent and collaborative work

Education

PhD in Oceanography/Marine Biology

Tools

R

Python

Job description

The postdoctoral researcher will join the Chemistry, Ocean and Climate (CHOC) research team at the Laboratoire d'Océanographie de Villefranche (LOV), CNRS, Villefranche-sur-Mer, France. The project SAF-SEA examines how marine organisms respond to multiple stressors (pH, temperature, oxygen, salinity) and aims to harmonize experimental approaches across European partners.

Responsibilities include designing innovative experiments, analyzing environmental time series, and contributing to